The basic RFID system consists of the reader,the reader’s antenna, the middleware, the data processing unit, and the tag(s). In many occasions, the middleware and the data processing unit refer to a single device, usually a computer. In addition, it is not uncommon for the antenna to be embedded to the reader, which is a welcomed combination when designing portable systems.
The positioning abilities of RFID are examined with the help of the parts shown in the table below.
Model: Garmin eTrex® 10
Model: CF-RU5112 – UHF long-range integrated RFID reader. Supported protocols: ISO18000-6B, ISO18000-6C (EPC C1G2). Operating frequency: 865~868MHz, 902~928MHz. RF output power of up to 30dbm, including 12 dbi antenna with effective range of 10–20m. USB interface and ability to output RSSI values from tag readings
Desktop reader & writer
Model: CF-RU5102 – UHF desktop USB reader & writer. Supported protocols: ISO18000-6B, ISO18000-6C (EPC C1G2). Operating frequency: 865~868MHz, 902~928MHz. Read range of < 20 cm. USB interface
UHF RFID PVC tags in card form-factor, compatible with the protocols of the readers above. Integrated chip (IC) model Alien H3, Impinj M4. Data retention: ~10 yrs
The first item in the list is the Garmin eTrex® 10 GPS unit, a consumer-grade portable receiver with the ability to log single points (Waypoints) or a series of recorded points forming a track (Track). It is compatible with both GPS and GLONASS. Once on, it starts collecting points and building the current track. It is possible to set the time interval of point collection down to 1 sec. The unit connects to the computer via USB as a mass storage device; the spatial data is automatically converted to GPX format and can be transferred by copying the files to the hard disk. Long-range reader
The original purpose of the CF-RU5112 RFID reader model is outdoor placement for car parking lot access control; in a typical scenario, the registered cars are equipped with plastic RFID tags in the form factor of a credit card. The tags are remotely interrogated by the reader and, provided their code is valid, the middleware sends the right command to open the gate.
The device has two cable connections; one for power and one for communication. Power requirements are covered by either a power adaptor connected to the mains or a portable battery pack system which can provide a voltage as low as 9V for a limited functionality. Communication with the computer is possible via the Universal asynchronous receiver/transmitter (UART) port and a UART to USB adapter. The UART connectors are used with serial communication standards similar to the serial ports found on personal computers, and allow for transmission of data at configurable speeds, which are configured by the controlling device (in this case, the computer).
The smaller RFID reader is a desktop USB device about the size of a modern smartphone. It can read and write data to the tags using the bundled software. The tags are credit-card-sized UHF transponders with a small internal dipole antenna. They came blank so they had to be assigned to a unique ID code with the help of the small reader/writer.
The manufacturer of the RFID readers provides the necessary driver software for the units to be recognized by the operating system (Microsoft Windows only) and the necessary resources for further development of customized applications; in addition, they offer a demo app which connects to the reader and runs the functions it supports.
RFID readers are relatively simple devices that essentially send and return packets of data. For the data to be actually usable, they need to be “translated” to more meaningful information, such as ID code of the tag that was read, date and time of the reading, and of course the RSSI data which is essential to the positioning technique described here. All these tasks are performed by middleware devices. In the present implementation, the role of the middleware is undertaken by a small portable computer running the manufacturer’s demo software and a “spying” software, which logs the data packet transactions, since no logging function is provided by the demo app.
The device connects via UART port, connected to a UART to USB adapter and then to the computer. It should be noted that UART hardware devices require that the data format the transmission speeds are configured by a separate microcontroller, in this case, the computer via the demo software, in the Reader Parameter tab, as shown in the screenshot above.
The USB interface hosts a COM port which the demo software opens in order to initialize communication with the device. In parallel, a third-party software is activated and set to “spy” data traffic on the same COM port that is being used. The spying software, Device Monitoring Studio by HHD Software, (hereafter the logger), is a multi-use data logger which is able to log data sent and received across serial ports following the RS232 or RS485 communication protocol standards (which are typically combined with the UART hardware connectors) and save the logs to the hard drive.
The data captured by the logger is exported to a text file, but a preview is shown in the screenshot below. The generated text appears when the demo is set to Query Tag mode, in which the RFID reader continuously transmits query signals until a tag is found in the interrogation area and it responds. The query intervals are controlled by the demo software and can be set to anything between 40–300 ms. The data corresponds with the expected interrogation bytes explicitly described in the RFID reader’s User Guide document.
Obtaining data from the GPS receiver
Consumer GPS receivers like the Garmin eTrex used in this project are built to be user friendly and easy to export data. As mentioned in previous paragraph, the recorded data is exported to GPX format the moment the device is connected to the computer.
The GPS Exchange Format (GPX) file is a XML text file with .gpx extension. As exported by the GPS device, it contains information that can be converted to spatial features (points) by the dedicated tool in ArcMap. Regarding its structure, tracks are registered by the tag set <trk> … </trk> and the logged points are found in between. The point entries do not have a serial ID number but they do have the following (in order of appearance):
Latitude (in decimal degrees)
Longitude (in decimal degrees)
Elevation (in meters)
Time (in the format: YYYY-MM-DD[T]HH:MM:SS[Z])
The GPX standard defines an <extensions> tag for each point, but Garmin’s devices do not use it. Instead, URLs are found in the header (before any track points) which point to external location with the ‘extensions’ information: they specify things like the spatial units, various data type declarations, units for features of more advanced GPS devices such as temperature and heart rate, and others. Projection is not defined in the GPX file because it is always the standard WGS84, meaning that spatial features that are either generated or are directly exported from GPX originals will need to be projected.
Obtaining data from the RFID reader
In order to get usable data from the reader, a special setup has to be made. This is because the manufacturer does not offer data logging software; instead they offer SDKs for the customer to build their own. Below is a diagram which outlines the principle of the setup.
Data transfer among the parts of the system occurs at all times of operation, even if no tag responds. The command that initiates interrogation is sent by the software. The first state of operation is the query state, where the reader continuously scans the environment of tags that might respond. This can be observed by monitoring the data transfer through the logger’s live interface.
Above is a screenshot of the log from the communication between the reader and the tag number 00 99. The code is not easy to read, so here’s an explanation.
The communication between the reader and the tag contains one or more command-response dialogues. The first dialogue is always the Inventory: the reader interrogates its environment by transmitting signals with the Inventory command repeatedly until a tag is found. When one or more tags are found within the interrogation zone, they capture energy, activate and answer back to the reader. This initial response comprises a few data values which communicate the tags’ ID codes.
After the response from the tag(s), the reader compiles a message to be sent to the computer that includes the IDs of all the tags that responded coupled with the measured RSSI values for each of the tags. The second dialogue is the Read Data: the reader sends one command chunk to all the tags within its range notifying them that the data they contain need to be communicated back to the reader, followed by a second command chunk that notifies which of the tags must answer. This concludes the basic “read that RFID tag” function. More functions are possible, such as permanent or temporary deactivation (kill and lock) of a tag, modification of the data stored in the tag’s memory (write), etc. These functions, however, do not communicate RSSI meaning that they cannot be exploited for RSS positioning techniques.
From the user’s perspective, the operation of the RFID follows the steps outlined below, provided all software and drivers have been installed and are ready.
Connect the UART end from the RFID reader to the UART-to-USB adaptor, and then to an available USB port on the computer
Run the demo software and open COM port
Run the serial port sniffer software, specify which COM port to “spy” and set the path where the log file will be saved. The log file is a text file without extension, and can be opened with a text editor.
Switch over to the demo software and initiate Query function
After initiating the Query tool (which runs the Inventory), the software sends commands to the reader periodically via the COM connection in the form of small data packets, which are then translated into functions.
The frequency of the commands is specified by the Read Interval option, which can be between 40 – 300 ms. An increased Read Interval value means fewer interrogations per second. In the log screenshot above, the direction of the data can be either Up (from Reader to Computer) or Down (from Computer to Reader).
Regarding the Inventory function, which is the one returning RSSI values, the interrogation command has the following data form:
An example request code for the Inventory function is the following:
06 00 01 04 00 ac 36
Hexadecimal numbers are written with the prefix 0x …, to distinguish them from decimals, so 0x0a is the same as 0a.
Length of the command data block in bytes, not including itself. The entire code is 1+6=7 bytes.
Address of the reader
Response command type. 0x01 is Inventory
Data parameter (Qvalue)
Data parameter (Session number)
Check byte – Least significant byte (LSB CRC-16)
Check byte – Most significant byte (MSB CRC-16)
EPC ID data chunks contain:
EPC-1 = TagID length + TagID data + RSSI
In the example of the screenshot above, the following response data code can be seen:
0a 00 01 01 01 02 00 99 c7 12 7f
The length of this code is 11 bytes, i.e. 11 hex numbers.
Length of the command data block inbytes, not including itself. The entire code is 1+10=11 bytes long.
Address of the reader
Response command type. 0x01 is Inventory
Number of the detected tag
Length of the tag ID, in this case,the next two bytes
First byte of the tag ID
Second byte of the tag ID
Check byte – Least significant byte (LSB CRC-16)
Check byte – Most significant byte (MSB CRC-16)
Cyclic Redundancy Check (CRC) is an error-detecting computation, similar to hashing and checksum functions used for large data sets. The two numbers (LSB and MSB) are calculated from the rest of the data internally by the reader; the computer can then recalculate them from the same data and compare the new LSB and MSB to the original ones. Transmission or communication error will have occurred if the two sets do not match.
Calculation of GPS accuracy
GPS points collected over extended periods of time are normally distributed over the true value. For shorter periods, they do not always average over the truth because the sample data may not be enough (Rutledge, 2010). A normal mixture of normal distributions is normally distributed, meaning that for datasets of extensive periods of time, points converge to the true value. For shorter periods of collection, Garmin GPS accuracy is calculated by determining the standard distance in the X and Y directions, then by calculating Circular Error Probable (CEP), and finally by calculating the 2DRMS (95%) radius (Coyle, 2012).
Standard distance is calculated as follows (Mitchell, 2005):
where and the coordinates for feature i, and are the mean centers, and n the total number of features.
Circular Error Probable is a statistical computation used in ballistics and GPS. For a target at coordinates (x,y), CEP is defined as a circle with center (x,y) and the smallest possible radius so that it contains all locations where there is a 50 probability of finding the true target. CEP is calculated as follows (Coyle, 2012):
Next, the 2DRMS statistic needs to be calculated which combines the vertical and the horizontal probability. However, ArcMap calculates the standard distance based on the combined probabilities of the X and Y coordinates, therefore it is not necessary to include it.
Analysis of the RSSI behavior
RSSI is an indicator based on the strength of the signal reflected by the tag. In order to conduct a pragmatic survey and directly calculate the signal levels, it is necessary to have a complete reference guide on the specifications of the equipment, which was not the case with the specific model. Therefore, the first step is to take measurements of RSSI values for tags placed at known distances in an obstacle-free environment to prevent signal degradation as much as possible.
The equation of propagation that links measured RSSI and distance was built based on the values in the table below.
Natural logarithms are converted to base-10 logarithms as follows:
After this transformation, the developed equation matches the RSSI equation:
The equation of propagation is solved for distance :
The reader is placed at an “unknown” position in a clear environment. In addition, tags are placed at relatively close positions ( < 15 m) away from the reader. The position of the tag locations is determined by the GPS. The reader, which is controlled by the portable computer it is connected to is activated and RSSI value collection takes place, against one tag location at a time. The RSSI values are statistically analyzed and the average value is used as input for the pre-calculated propagation formula, which extracts a value of distance. Distances to all the tag locations are combined with trilateration calculations and a relative set of coordinates is produced (x,y), which is converted to global coordinates.
For a 2-dimension cartesian system, let unknown point, , , points with known coordinates, and , , distances between and , , respectively. The coordinates of the unknown point are the solutions of the system:
In the trilateration testing phase, tags were placed at locations and their positions were measured using a GPS. For each of the points, the most accurate data collection session was used, and their mean center was regarded as reference point. The coordinates had to be converted to UTM (projection WGS 1984 UTM Zone 33N) to correspond with the cartesian system. Using the generated model, the measured RSSI values are translated to distances.
Ideally, the system has solution . The system would ideally be represented by the figure below.
However, the system has solution within an area encompassed by arcs (ab, bc, ca).
Causes of imperfect position estimation are:
Locations of reference tags are GPS-based
Combined (pooled) variance does not include model error
Radio-based identification systems were originally designed to monitor and identify objects, animals and people based on the proximity of a transponder to a reading device. After simple modifications, the same system architecture can be regarded as a tool for estimating the position of objects or people bearing transponders, since they are radio transmitting devices, when they are found within the operation range of the system. The location of the agent is then determined relative to a reference location (Bouet & dos Santos, 2008). RFID localization systems are more commonly found indoors, in buildings where the quality of satellite positioning service is dramatically weakened. Examples of applications are in construction sites and storerooms.
RFID positioning implementations face problems similar to the problems of other radiowave-based positioning systems, such as multipath, shadowing, interference. Especially for signal-strength ranging techniques, these systems are even more prone to the effects of the environment, however, a strong correlation between the strength of the signal emitted by a base station and its distance does exist. This correlation can be exploited for location estimation (Locher, Wattenhofer, & Zollinger, 2005).
The methods used for RFID positioning make use of the design and specifications of the technology. For indoor location systems (but can also apply to outdoors) that use range-based distance measurements, the methods can be classified into two categories: received signal strength (RSS) methods and time-of-fly (ToF) methods. The methods of first category function under the law of signal decay by distance, while those of the second category are based on the signal propagation physics. Methods such as time-of-arrival (ToA), time-difference-of-arrival (TDoA), angle-of-arrival (AoA), and phase-of-arrival (PoA) are examples of ToF methods. ToF methods are also used in satellite positioning systems, such as GPS (López, Gómez, Álvarez, & Andrés, 2011).
Each one of the methods mentioned above comes with its advantages and disadvantages. The general opinion expressed in the relevant literature is that RSS methods are not as accurate and reliable as the ToF methods due to the fact that distance is only one of the numerous parameters that affect the RF signal strength. However, RSS methods are simple in their setup and do not require expensive equipment. The RFID technology
Radio frequency identification (RFID) systems consist of transponders (tags) and readers that communicate using electromagnetic energy at the radio spectrum, hence their name. RFID systems can be classified according to their energy source into three types: passive, active and semi-active. In a passive system, the tag draws all the energy it needs to function wirelessly from the reader. On the other hand, in the active type the sole purpose of the reader is to communicate with the tag and not to provide power, which is provided by another source, usually a non-replaceable internal battery. A semi-active RFID system uses tags with an internal battery which solely powers the internal circuitry for the tags internal functions, while the energy provided by the reader is only used for the activation (wake up) of the tag and the communication (transmission of data).
The technology behind identification using radio waves is far from new, as it dates back to the beginning of the century and especially in the time before the Second World War, when a simple implementation called IFF, acronym for Identification, Friend or Foe, was used for identifying approaching aircrafts and avoiding friendly fire. A custom antenna was mounted on the friendly aircrafts; it was designed to answer to interrogation electromagnetic signals emitted by ground stations. Precursor to the modern passive RFID systems, however, is the “Thing”, aka the “Great Seal Bug”; a small eavesdropping device invented by Léon Theremin that was used by the Soviet intelligence agencies to spy on the Americans. The device had a simple microphone connected to an antenna. The device was powered wirelessly by a remote source in a manner similar to the modern passive RFID systems.
In the industry, RFID is widely regarded as a replacement for barcodes. When compared to previous Auto-ID technologies, RFID has significant benefits and some drawbacks. Amongst the main advantages is the ability to communicate and track items without physical contact and out of line of sight, the increased automation in reading/writing and the fast and accurate data transfers, the larger amount of programmable data they can store, and the flexibility and robustness of the entire system. When used in a positioning application, these advantages are of particular importance. However, some disadvantages do exist, the most important of which are the potential incompatibilities between devices coming from different manufacturers, the increased implementation costs and the cost of the equipment, and the fact that the propagation of radio waves is affected by the environment and even blocked by some materials such as water and metallic surfaces (Erande, 2008).
Standardization of RFID was proposed and has been developed by the International Organization for Standardization (ISO) and by the Auto-ID Center with their Electronic Product Code technologies.
RFID system architecture
In its simplest form, a fully functional RFID system consists of two main components: the transponder (tag) and the reader (a.k.a. RFID scanner) (Finkenzeller, 2003, p. 7). The transponder is a device generally simple and cheap that is attached to the object or person being tracked/identified; the reader is a larger, more complex and more expensive unit that can power, read, and write the transponder without physical contact and when the latter is placed within the reader’s read range. The image below shows a typical RFID setup used in logistics. This setup is the basis for a positioning system, where the distance between the antenna and the objects bearing the tags is to be estimated.
The transponders used in RFID systems typically consist of three sections: a radio-frequency front end, an analog section, and a digital section. Not all sections are present in all kinds of tags; for example, the very simple 1-bit tag usually found in antitheft systems does not have a digital section. The role of each section is outlined below.
RF front end:
Energy harvesting from the electromagnetic field
Demodulation of the received signals
Transmission of the outgoing signal
Clock of the digital subsystem
Powers the rest subsystems/components of the tag chip
Stabilizes the voltage of the front end
‘Power on reset signal’
Manages the power
Executes the protocol operations
The Reader consists the first communication layer between the tag and the rest of the RFID system. They consist of subsystems that enable the transmission of energy and data to and from the tag and the forwarding of the data to the next communication layer in the system; these subsystems are typically a RF unit, an external (or internal/embedded) antenna, an electronic control unit, and a communication interface. Readers communicate with the middleware, or another control device, through the communication interface (e.g. RS 232, RS 485, USB, UART).
Readers of passive tags:
High-power (up to 4W) RF transmission for the activation of the passive tags
High power consumption, in the order of Watts
Maximum distance of communication in the orders of centimeters or meters (up to 20 m)
Reading capacity of ~100 tags in a few seconds
Readers of active tags:
Low-power RF transmission (10–20 mW)
Power consumption requirements significantly lower (~mW), which facilitates the design of portable readers
Long reading range
Reading capacity of hundreds of active tags in a few milliseconds
The interface that connects the reader with the other units down the communication chain is referred to as “middleware”. Middleware units perform the following tasks:
Filtering of the data incoming from the reader, so that the system is not overwhelmed
Routing of the filtered data towards the proper software application
Management of technical parameters, such as reading frequency, transmission energy levels, et al.
The most meaningful categorization of RFID systems is based on the power requirements of the tags. Three categories can be recognized” passive, active and battery assisted passive, aka “semi-passive”. Passive tags are powered entirely by the reader and constitute the simplest design of all three; active tags have an internal power source, usually a small, non-replaceable battery which provides energy not only for the transmission of the signal, but also for internal circuitry functions; battery-assisted tags embed a small battery which is used only for the internal functions of the tag, while communication with the reader requires passive-like energy harvesting. The capabilities of a tag are greatly affected by the energy source it uses. For example, the maximum reading distance of an active tag is significantly greater than the range of an entirely passive system, which can be from a few centimeters (NFC devices) and can reach 10–15 meters maximum for UHF systems. For comparison, the maximum reading distance of an active tag can exceed 100 m. In general, semi-passive tags are the middle ground between entirely active and entirely passive systems.
The number of RFID system types available in the market today is extremely large because of the different parameters and characteristics of the architecture of the technology. As with the personal computer market, where the buyer has to take into account the characteristics of a number of subsystems that identify a specific model like the CPU, the RAM the HDD etc., a number of ‘selection criteria’ (Finkenzeller, 2003, p. 25) are present in a RFID system. These criteria are:
The criteria are not entirely independent; systems that operate at higher frequencies tend to have a higher range. For example, microwave systems operating at frequencies in the vicinity of 2.4 GHz can typically achieve read distances of tens of meters, while systems in the LF bands have range of only a few centimeters. The architecture of the systems is comprised of a number of properties that together define the system itself.
Of significant importance to a RF-based positioning system is the maximum distance of communication between the transponders and the agent. The range of RFID systems is dependent on its design and spans from a few centimeters to over 100 m. According to (Finkenzeller, 2003, p. 26), the key factors of an application that will define the range are the ‘positional accuracy of the transponder’, the presence and the number of transponders.
The operating frequency of a RFID system dictates may other parameters, such as the range and the environment they can be used in mainly due to different permeability. The most established bands in the industry are four, but more can be defined. The four bands are: LF, HF, UHF, SHG or microwave.
30 GHz – 3 GHz
2,45 GHz; 5,8 GHz; 24,125 GHz
3 GHz – 300 MHz
433,920 MHz; 869 MHz; 915 MHz
300 MHz – 30 MHz
30 MHz – 3 MHz
6,78 MHz; 13,56 MHz; 27,125 MHz
3 MHz – 300 kHz
300 kHz – 30 kHz
30 kHz – 3 kHz
For an entirely passive RFID-based positioning system, the best choice seems to be a system operating in the UHF range, where maximum read distance can be up to a few meters. For battery- assisted or active systems, one may opt for a microwave system offering significantly higher range in the orders of tens of meters. Passive LF systems were amongst the first that were used and became widely popular in animal tagging applications and in high-accuracy timing systems used in sports. There systems are low-power, the reading range is a few centimeters and data rates are very low, usually under 8 kbps. LF requires large antennas because the coupling is inductive and reading range is within centimeters. Similarly, HF systems are found in book tracking in libraries and smart cards, and have a reading range of up to 1 m, which is insufficient for passive positioning.
The demand for small size and low-cost RFID tags has an impact on the size of the memory they can have. In general, cheap and low-capacity memories store identification data only, while advanced (and more expensive) “smart” tags can afford higher capacity memory and circuity. Typical memory technologies used in RFID tags are:
16 bytes – 8 kb
104 – 106
256 bytes – 64kB
Tag memory can be used for storing location data, such as coordinates, or semantic position data, such as cell ID. This can be useful in autonomous systems that do not have access to a spatial database from where to retrieve location data.
In sensitive RFID applications, for example where transaction of personal identification data takes place, security needs to be included.
Passive communication between reader and tag
Data transfer between readers and transponders in an RFID system presupposes the establishment of a communication channel between the two devices. Two methods are described: inductive coupling, which is used for systems operating at LF and HF bands, and modulated backscatter coupling for UHF and higher bands.
In resonant inductive coupling, the reader antenna has a coil which is powered by alternating current generated by an internal oscillator. As the electric current passes through the coil, it generates an alternating magnetic field that serves as a power source for the tag. The latter’s antenna coil is energized by the electromagnetic field which subsequently charges a nearby capacitor and activates the tag’s integrated circuit. Data transfer takes place through the electromagnetic energy exchange, which occurs in pulses translating to data.
Backscatter coupling is used in UHF tags and requires a dipole antenna. The reader generates high- power electromagnetic signals that the tags modulate and reflect back to the reader. Some readers can sense the power levels of the reflected signal, which is the basis for return-signal-strength positioning.
Sequential, Half-duplex, Full-duplex
RFID tags that could have any use in a positioning system must be able to harvest energy and transmit data that is stored in their small chip. Both of these actions occur through the only antenna system that the tag has, it is therefore necessary to define the timings for energizing, receiving data, and transmitting data. Three alternative procedures are used: sequential, full-duplex and half-duplex. All three procedures use three lanes of operation that “pass through” the single hardware interface, i.e. the antenna. The first lane is the energy transfer, the second lane is the downlink (data transfer from reader to tag), and the third lane is the uplink (from tag to reader).
The main characteristic of the sequential procedure is the intermittent energy transfer from the reader to the tag. Energy and data are transferred simultaneously in the first time slot, which is followed by uplink-only activity in the second time slot, followed by a simultaneous energy transfer/downlink activity, and the cycle continues until it is terminated with an uplink activity that is not followed by energy transfer.
Half- and full-duplex procedures utilize a constant energy transfer and non-constant data transfer. Of the two lanes for data transfer (uplink and downlink), only one is active at any given time slot in half- duplex procedures, while full-duplex procedures have them operating in parallel.
RFID positioning approaches
Positioning approaches that implement the lateration technique on RFID systems include Phase of Arrival (PoA) and Phase Difference of Arrival (PDoA), where the phase of the signals are used for estimation of distances between the tags and the readers (Povalač & Šebesta, 2010) and are found in systems operating at the UHF range.
Proximity-based (cell-of-origin) RFID methods
The extent of the interrogation zone of an RFID system defines the granularity of the proximity-based location methods, where the approximate location of the agent equipped with a tag is determined as they move into the zone. Proximity-based positioning might better be described by the term “tracking” (Song, Haas, & Caldas, 2007) rather than positioning because as a method, it does not necessarily disclose the geographical position, absolute or relative, of the tag, but it has a semantic meaning, e.g. “tag is found at gate B17”. The position of “gate B17” is therefore the position of the tag, to a certainty characterized by the granularity of the system.
When this technique is used for mobile phone tracking using the cell tower they are connected to, it is generally referred to as Cell-of-Origin (CoO), but this term could also be used in RFID.
Proximity-based methods are meant to be used in occasions where no information on the distance between the agent and a key location (in this case, a properly placed transponder of known coordinates) is available, such as strength of returned RF signal, propagation time for the signal, angle of arrival, etc. These methods offer the following benefits:
Reduced cost of equipment. RFID readers and systems with the ability to return or extract some kind of information regarding the propagation parameters that can later be used for positioning estimation cost significantly more. For the present thesis, the purchased RFID reader model without the return signal strength data output would have cost approximately 30% less. The cost increases in more complicated systems based on time-of-fly or angle.
Higher certainty in precision. Precision in PBPs is tightly linked to the interrogation range of the tag or the overlapping tags, and can be adjusted from the settings panel of the RFID system.
The drawbacks of PBP systems are the following:
Number of tags. For a system to be able to perform as mentioned above, the precision of a PBP system is linked to the read range.
For a 2D implementation, the unknown Cartesian coordinates of an agent x1,y1 that has interacted with a transponder of known coordinates x0,y0 will always be found within the radian interrogation range r of the transponder/reader system. In other words, assuming that the signal is uniformly homogenous and unobstructed, the following expression holds:
In this example, as the agent moves in the interrogation range of the tag, i.e. as the distance between the agent and the tag becomes d≥r, the reading process is activated and the location of the agent is registered at high accuracy and at precision equal to r. Higher precision can be achieved by reducing the transmission power of the RFID reader, thus reducing the r; noted that the effective positioning range for that specific tag will also decrease.
Higher precision can be achieved by placing several tags at close proximity with overlapping interrogation ranges. Estimating the location of the tag comes down to selecting a point from the intersecting region, which should be smaller than the entire range of a single reader. However, this implementation must be using anti-collision and anti-interference systems otherwise quality readings will not be possible.
RSS-based RFID methods
RF signals can be represented by the following units of measurement, which can be more or less converted from one another for measurements that are not close to the extremes and with a level of accuracy that varies (Bardwell, 2002):
mW (milliwatts), where
RSSI (Receive Signal Strength Indicator)
Milliwatts (mW) and db-milliwatts (dBm) are measurements of the emitted RF energy but mW is linear, while dBm is logarithmic. Conversion between the measurements is possible by calculating the common logarithm (base 10) and multiplying by 10, as shown in the example that follows:
Measurement in mW
Measurement in dBm
10 ∙ (log100) = 10 ∙ 2 = 20
10 ∙ (log50) = 10 ∙ 1,69897 ≈ 17
Doubling the emitted RF energy increases the dBm values by approximately 3 dBm. In addition, while it is possible to have negative dBm values, this is not the case with the mW since the latter refers to emitted RF energy which does not make sense to be negative.
RSSI is usually expressed in the form of a single byte character with value range 0-255, although many vendors of consumer products generally report it in a ‘percentage’ scale of 101 values, where0 refers to minimum signal strength and 100 to maximum. This is mostly found in devices that are not related to RFID, such as wireless network (WiFi) devices etc.
A is the RSS at 1 m distance
d is the distance
n is the signal propagation exponent
Attenuation is expressed as ratio of change, in dB (decibels). For two power states P1 (measured) and P0 (reference), where 0 < P1 < P0, attenuation is calculated as (Couch, 1999, p. 212):
At this point, it should be pointed out that RSSI and distance are reversely related, i.e. higher distance will give lower RSSI returns.
Applications of RSS
Signal-strength-based ranging systems have been used in robotics for adding depth (distance) data to the pixels of tagged objects as they are seen by cameras attached to the robot (Deyle, Nguyen, Reynolds, & Kemp, 2009), in locating systems used for tracking of people in hospitals, livestock, workers in construction sites (Choi, Lee, Elmasri, & Engels, 2009). In fact, RSS-based positioning in construction sites has been the focus in quite a few projects, as the special requirements of these environments require study of interference and filtering of the signals prior to the actual location finding algorithms (Ibrahim & Moselhi, 2015).
RSSI value fluctuations
In practice, the strength of the returned signal is not stable, experiencing variations that are caused by several factors. These factors have been found to be physical distances between the transponders, obstacles, orientation of the antennas, and interference (Chapre, Mohapatra, Jha, & Seneviratne, 2013). These fluctuations, however, could be used for building of spectral maps in positioning using the fingerprinting method, were each cell must be assigned to a unique set of attributes in its spectral signature.
Fingerprinting in RFID
With fingerprinting, location determination takes place over an area that has analyzed beforehand. It is a two-step setup that requires a preparatory phase (calibration) and the actualization phase, where real-time localization occurs.
In the first phase (calibration), the entire area is divided into cells and RSS value samples are taken in each cell, with coordinates (xi,yj). The data of the most representative values is stored in a database (lookup table, location fingerprint map or radio map) and remains available for the next phase. By “most representative values”, it is meant that many RSS values are measured but only the average values are kept (Kaemarungsi & Krishnamurthy, 2004). The survey area can be monitored by several RFID readers (Ting, Kwok, Tsang, & Ho, 2011). The level of granularity in fingerprinting is characterized by the cell size which is defined by the distance between each set of coordinates (x1,y1), (x2,y2), …
In the second phase (localisation), the reader scans the environment for tags and receives RSSI values for each tag, which are communicated to a pattern-matching algorithm. Location is determined by querying the database for a matching cell record (xi,yj). However, it might not be possible to match the measured RSSI values to an exact cell, therefore an algorithm of Nearest Neighbors is used (Guvenc, Abdallah, Jordan, & Dedeoglu, 2003) which matches the unmatched measurements to the database records where the Euclidian distance is minimum. It should be noted that it is important that the conditions during the mapping phase and the positioning phase are exactly the same in order to limit the variations in the signal strength measurements.
As the signal leaves the source, it propagates through the medium (or the free space) surrounding the source, which is generally filled with background noise, towards the receiver. Based on the reaction of the receiving unit with regards to the signal, three zones can be defined:
Transmission zone, where communication between the transmitter and the receiver is possible without any errors (or with an insignificant error rate)
Detection zone, where the signal is detected but the error rate is too high for actual communication, and
Interference zone, which is the space past detection range, where background noise “covers” the signal rendering practically undetectable by the receiver.
Multipath particularly affects UHF tags because of the fact that communicate via backscatter, i.e. by reflecting the reader’s interrogation signals. In addition, RFID is popular in tagging applications, meaning that it is expected to find them in confined places, such as indoors, and in places with many obstacles, such as storerooms. Multipath can be filtered out using a statistical profile for each ID (Wang & Katabi, 2013).
Collision of tag communication is caused by the presence of many tags in a confined space. In such cases, the reader is prevented from communicating properly and signals cannot be registered; a problem that appears in logistics but can also appear in a GIS application. Each communication path opened between the reader and one tag acts as interference to the path between the (same) reader and the nearby tags. If less dense placement of the tags is not feasible, then anti-collision systems (protocols) address these mishaps using the following techniques:
by forcing the reader to time-manage the readings, lock communication channel with one tag at a time and block all the others,
Block all others and open alternative communication channel
Use time-sharing so that each tag has a time slot to communicate
Install to tags a subsystem that sorts them in the reading queue according to their distance from the reader
(for moving tags) install to tags a subsystem that sorts them in the reading queue according to their relative speed in relation to the reader
Anti-collision protocols in RFID is a field of ongoing study and new techniques emerge at times.
When designing a model for a system that uses RFID for positioning, the following two aspects need to be taken into account:
False-negative readings, meaning that a tag is not detected, even though it lies within the reader’s read range. According to (Hähnel, Burgard, Fox, Fishkin, & Philipose, 2004), false- negative readings are frequent in these RFID model scenarios. In positioning applications, false negatives will affect the accuracy of the system by denying the third required distance measurement (for 2D systems). The effects of false negatives can be mitigated if the agent is moving in space covered by the read ranges of multiple tags by implementing correction algorithms (probabilistic distance-aware models) which function under the principle that there is a minimum travel time between the nodes and a minimum number of tags a traveler must encounter, effectively assuming the presence of a node if it’s been too long since the last tag was encountered (Baba, Lu, Pedersen, & Xie, 2013).
False-positive readings, where the reader detects a tag located at a distance greater than its maximum read distance, as specified by its manufacturer.
The parameters that affect the aforementioned false-readings are the orientation and positioning of the tag with respect to the reader(s), the material environment and the presence of objects foreign to the RFID reading process. In particular, the orientation of the antennas affects the minimum required RF energy for a tag to be read correctly, i.e., more energy has to be radiated by the reader and absorbed by the tag in the case of non-ideal alignment of the antennas. Regarding the material environment parameter, a reader’s read range field is shaped by the materials used and their positions. The material of the surface (or underneath) where the tag is attached can absorb part of the RF energy thus reducing the maximum read distance for the said tag; an example of such a material is metal. False-positive readings can be caused by objects that reflect the RF waves in such a way that communication between readers and tags that seemingly should not have been read may occur (a.k.a. multipath contributions).
Data obtained by a wireless positioning device such as a GPS or, as in this case, the RFID reader, contains noise that needs to be removed prior to feeding the data to the model. This task is often carried out algorithmically by the Kalman filter (Kálmán, 1960), a mathematical tool that is used in cases where input data contains statistically independent noise that needs to be removed. Kalman filters are widely used in signal processing (Welch & Bishop, 2001) and function under a “predict- correct” algorithm. In the predict phase, the state of the system and its error covariance are projected one step ahead, and in the correct phase, the estimation corrector is applied on the real-time measurement. As a result, the data feed is smoothed as shown in the diagram below:
Designing the RSS-based system
As previously explained, the beneficiary’s location is estimated by the positioning system in relation to a reference position and later extrapolated to a common system of coordinates. In the case of RFID, two components of the system can play either role—that of the reference position, or the one of the unknown position. These two components are the tags/transponders and the readers; either the position of the tag is estimated relative to the position of the reader or vice versa. Each of these implementations have strengths and weaknesses. The final choice will be based on the following considerations:
Physical properties of the beneficiary of the positioning service. For example, if the objective is to increase the accuracy of GPS in driverless cars moving in dense urban environments, then it is possible for the cars to carry a somewhat heavy and bulky long-range UHF reader and perform ranging to fixed passive tags. On the other hand, for tracking of livestock in a farm, it would make more sense to install the readers on fixed positions and tag the animals with the much lighter passive tags.
Power. Readers need to be connected to a sufficient power source, while tags are passive. As an example, a typical 9V battery can power a long-range UHF RFID reader like the one used in the present thesis for up to one hour of continuous operation.
Cost. Passive tags are inexpensive (indicative price from USD 0,20 per piece), while long- range readers are significantly more expensive (indicative price USD 220,00 per unit). Installation of readers incurs additional costs as well.
Reader-to-reader interference, reader-to-tag interference, and multipath effects caused by overlapping interrogation ranges in multiple-reader implementations (Bekkali, Zou, Kadri, Crisp, & Penty, 2014).
In this study, the concepts of positioning and location sensing are explored with a focus on RFID technology as applied in urban environments. RFID positioning is a simple and cheap way to identify the location of an object or a person, indoors and outdoors, alone or in combination with different positioning methods, such as satellite systems. The implementations are numerous and an asset to location-based services and GIS.
1. Theoretical foundation
1.1. RFID technology and principles
Radio Frequency Identification, generally abbreviated as RFID, finds its origin in military applications decades ago, when backscatter radiation was used to identify hostile aircrafts during World War II. The technology has evolved since then, enabling more complicated implementations, such as supply chain monitoring, product labeling, and item tracking. Currently deployed RFID systems are generally focused on the real-time tracing, tracking and monitoring, with numerous implementations in logistics, inventory management, supply chain and access control, to name a few. The key features of the technology have helped to find its place in industrial and urban settings: being contactless, no line of sight communication, and automation that does not require any human intervention (Goshey, 2008). RFID uses wireless radio frequency (RF) communication technology to interlink remote devices, usually with a unique ID (UID), and transfer data among them for further processing.
The following four components are found in a basic RFID system:
Transponder, or tag
Transceiver, or reader
Antenna, which is attached to the reader
Reader interface layer, which takes the role of middleware and runs tasks like the filtering the readings, keeping logs and forwarding the read data to the next level of analysis.
RFID tags are found in the following three types: passive, active and semi-active. This categorization refers to the energy requirements and the method of energy consumption for the tag. Active tags are powered entirely by a battery, while passive tags are powered exclusively by the electromagnetic waves emitted by the reader. The third category of semi-active tags includes transponders that use an internal power source (battery) only for their internal functions like storing data to memory and microprocessing; the communication between tag and reader is done by consuming energy provided by the reader similarly to the entirely passive tags. Passive tags can be very small and cheap, however the maximum read distance is shorter than the active type. On the other hard, active tags can transmit to the reader from a longer distance, but the battery has a limited life (Shikada, Shiraishi, & Takeuchi, 2012).
The passive communication method generally involves three main phases of operation, as described below:
Charging phase: energy is transmitted by the reader towards the tag, which in turn collects it and temporarily stores it in its circuitry.
Reading/communication phase: the tag uses the stored energy to transmit data back to the reader.
Discharging: this phase is necessary in some systems where not all of the absorbed energy is consumed after the reading operation. After the discharge, the tag is reset and ready for a new reading. Discharging phase may require significantly more time to complete than the reading phase, which must be taken into account when designing a RFID-based positioning system.
Tags and readers communicate with each other mainly by either inductive coupling or by electromagnetic backscatter coupling (electromagnetic waves). Inductive coupling is used for RFID systems operating at frequency bands LF and HF (Table 1), while electromagnetic backscatter is used for higher frequencies.
The frequencies that RFID systems use to communicate range from the Low Frequency (LF) part of the spectrum (<135 kHz) to microwaves (2.45–5.8 GHz). Water and other nonconductive substances significantly absorb frequencies around 1 GHz, so this frequency range is not used (Finkenzeller, 2010). Several properties are dependent upon the operation frequency, such as the read range, the penetration of the signal and the data transfer rate. Table 1 gives an overview of different frequency band characteristics.
2,4 GHz – 5,8 GHz
Toll roads, train wagon positioning
up to 100 m
30 MHz – 3 GHz
Luggage management, pallet-level tracking in logistics
2 – 5 m
3 MHz – 30 MHz
Libraries, smart cards, item-level tracking
up to 1 m
30 kHz – 300 kHz
Farm animal tagging, car immobilizers
Table 1: Common RFID frequency bands and application examples
Higher operation frequencies are generally related to higher data transfer rates. Applications of tracking objects moving at a high speed, such as a speeding car or a train wagon, require relatively high data rates, otherwise the tag will not be at a reading distance from the transceiver long enough. However, data rate is not the only parameter affecting the maximum tracking speed, since there are other factors such as the necessary charging time for the passive transmission methods. In practice, the communication can take nine times longer that the theoretical limit of the data transfer according to (Chon, Jun, Jung, & An, 2004), who showed that a reader moving at a speed of 165 km/h can read 128 bits from a road tag as long as the read range is greater or equal to 81 cm.
Of particular importance to a GIS-based implementation of an RFID system are the characteristics regarding memory and data transfer of the communication path tag-reader. RFID tags come in a variety of memory sizes, from 16 bytes to 64 kB, using ferroelectric random access memory (FRAM) (Fujitsu, 2014).
1.2. Location-based services
In the context of the present project and geography in general, positioning refers to the act of a localization system and its relation to a location-based service (LBS), which is a product of combination of contemporary informatics, the Internet, and geography (Figueiras & Frattasi, 2010). An LBS is an informatics service that serves its user in a manner relevant to their location. A number of definitions have been given to the LBS, all relating to a, chiefly mobile, user and their interaction with technologies of informatics.
Examples of LBS are (Kolodziej & Hjelm, 2006):
Intelligent information management in Wi-Fi hotspots, where connected users are presented with (or are blocked from accessing) information relevant to their location, such as ads in airports and blocking of certain websites, like social media and other social information sharing sites, in military bases.
Notifications to visitors of exhibitions, according to the kiosk they are closer to.
Tracking of newborns in maternity clinics using small RFID tags, to eliminate baby stealing.
Emergency crews share their positions so than an unfortunate event can be handled more effectively.
In all of the examples above, the key input for the LBS is of course the location of the users, which is determined by various means. Of particular importance is the meaning of the term “location”, which generally refers to a physical place but it can also be associated with non-tangible concepts. Physical locations, relevant to LBS implementations, are subcategorized by (Küpper, 2005) as follows:
Descriptive locations, which are referenced to with the help of elements of nature (e.g. “next to that rock”)
Spatial locations, which are the positions in space defined with the help of a system of coordinates (Euclidean, polar, etc.)
Network locations, ‘virtual’ places in a network topology, as defined by a unique address (IP address, etc.)
A distinction is made between physical and virtual locations, the latter being locations in a virtual system, like a video game, a chat room, an instant messaging (IM) application (Küpper, 2005). However, the aforementioned examples generally still point to a specific network location: a website is stored on a server with a specific IP address, translated by a domain name system (DNS) to a human-friendly Uniform Resource Locator (URL).
1.3. Fundamentals of positioning
1.3.1. Cell ID
The term refers to cell identification. In this case, the position is identified according to the location of the cell the object of interest is ‘connected’ to. This approach is generally used in combination with others, for example in an RFID positioning system, the reader’s ID number would be the Cell ID whereas in a WiFi network, the MAC address of the wireless module would be the Cell ID. In theory, this method is used for object tracking with an RFID system in symbolic space, where no coordinate system is used per se, but tracking takes place in the form of true-false logical functions, for example “if the tag T is in the area of coverage of reader R at time t, then the function F is true, otherwise (else) false” (Kang, Kim, & Li, 2010)
1.3.2. Signal strength
Radio signals propagate in space but due to fading caused by destructive propagation effects, such as signal attenuation, shadowing, scattering and diffraction, they lose strength. The loss of power is a function of distance between the transmitter and the receiver, it is therefore possible to calculate how far the tracked object is from the transmitter based on the Received Signal Strength (RSS), calculated in dB, or the Received Signal Strength Index (RSSI). However, signal-strength-based positioning can be very inaccurate compared to the techniques below because it is very difficult to be calibrated and because the signal strength is affected by many factors, distance being only one of them.
1.3.3. Time of arrival
The principle of this method is the fact that electromagnetic radiation propagates in space at a finite rate, equal to the speed of light c. Very accurate clocks must be used, which set timestamps when a signal is received at the tracked object. By knowing the propagation speed and the time it took the signal to reach the destination (receiver), it is possible to calculate the distance. In this method, however, the concept of clock granularity is delved, which generates the need for special mechanisms to correct errors due to the non-contiguous function of the clock, and therefore the timestamp operation, which runs with clock ticks at specific frequency.
The position of the tracked object is estimated by triangulation the measurements of distance through the technique of lateration. In a two-dimensional space, three measurements of distance are required, while in three dimensions, four measurements are required. The location of the receiver is at the point where the three (2D) or four (3D) circles intersect, as illustrated in Image 1.
1.3.4. Angle of arrival
The angle at which the signal leaves the transmitter and reaches the receiver is the key to this method. However, such a system requires high cost directional antennas and is generally more complicated than the Time of Arrival method. The measurements of this method are used in the process of angulation, a kind of triangulation that calculates the position of the tracked object based on the angles of at least two signals transmitted from two different sources.
1.3.5. Phase of Arrival (PoA)
The principle of this method is based on the difference in the phase of the signal emitted by the reader and the signal backscattered by the tag that is read, which is related to the distance of the backscatter device. This distance estimation technique is often used in combination with other techniques, such as AOA. It is possible to design a system with multiple frequency pairs that will be able to estimate the location with a higher accuracy (Zhang, Li, & Amin, 2010).
2. Positioning systems
The term positioning refers to a technique that can determine and share the location of living and inanimate objects continuously and in real-time. According to (Esri, 2006), positioning can be either static (determining a position on the earth by averaging the readings taken by a stationary antenna over a period of time) or kinematic (determining the position of an antenna on a moving object).
An LBS can deliver services and exchange data among static and moving users. Advanced location sensing mechanisms need to be deployed, as indexing of moving objects that use techniques to “exploit the volatility of the data values being indexed” (Jensen, Lin, & Ooi, 2008), which is a characteristic of objects that move into and out of the area covered by the LBS. In the example of a –Tree indexing of moving objects, a positioning system needs to be able to capture a position vector , a velocity vector and a specific time value when these inputs are valid.
The most known positioning technique is the one used by satellite navigation systems, where the satellite positions are known and the time the signal needs to propagate to a target object is measured. Assuming that the electromagnetic waves travel at the speed of light, the distance between the satellite and the target object is calculated by multiplying propagation time by speed of light. In reality, however, electromagnetic radiation experiences an atmospheric delay, which is defined as the reduction of their propagation speed when they pass through the ionosphere and the troposphere.
2.1. Outdoor location sensing
The technologies dominating outdoor positioning are mainly based on satellite systems, such as the Global Navigation Satellite Systems (GNSS). Such systems have now reached maturity and have a number of advantages: high precision, continuity, able to function regardless of the weather conditions, near-real-time observation, and increased reliability. With regard to their advantages, GNSS are widely used in applications other than positioning and navigation as well, such as remote sensing (Shuanggen, Estel, & Feiqin, 2013). The most widely used satellite systems for global navigation and positioning are:
GLONASS (USSR, now Russia)
Compass, aka BEIDOU (China), launched in 2007
Global Positioning System (GPS) finds it roots in the early 1960s, when a number of U.S. governmental organizations joined forces to develop a system that would provide navigation and positioning services primarily for military and secondarily for civilian use. At present, the American positioning system consists of a constellation of 24 operational satellites at an orbital radius of approximately 26 600 km, and has been used not only for positioning and navigation, but also for delivering precise timing.
Former USSR had developed their own satellite positioning system, known as Global Navigation Satellite System (GLONASS). The constellation of its satellites was completed in 1995, four years after the collapse of the Soviet Union in 1991. The project’s operation was suspended for the rest of the 1990s but is now operational with 24 satellites (Russian Federal Space Agency, 2016).
The European Space Agency’s (ESA) ongoing GNSS project Galileo, named after the Italian astronomer Galileo Galilei, is a €5 billion project intended for civilian and commercial use. The system will become fully operational until 2020, but the initial services will be made available in late 2016. In full deployment, the system will consist of 24 operational satellites at 23 222 km altitude; it will provide basic, relatively low-accuracy services to everyone and advanced, high-precision services to paying customers (European Space Agency, 2015).
The fourth main GNSS is the Chinese BeiDou/COMPASS, scheduled to deploy by 2020. The system’s constellation will have 35 satellites, five of which in geostationary orbit, and will be offering basic services for civilian use and higher accuracy services for special uses, similarly to the systems mentioned earlier (BeiDou Navigation Satellite System, 2015).
2.2. Indoor positioning
Satellite technologies have well met the needs for determining the location in outdoor environments. However, they are not well suited for indoor areas because of the poor reception of satellite signals, which are greatly degraded inside of buildings. The task of positioning in such environments has been addressed by several techniques based on wireless technologies. Examples of such systems include sonars (audio waves), radio signal triangulation and beacons (electromagnetic waves) (Fernandes, Filipea, Costa, & Barroso, 2014), as well as infrared and physical contact methods (Youssef, 2008).
Indoor positioning faces significant challenges that place it in a less widespread position, when compared to the extensive use of ‘conventional’ GNSSs. These challenges are (Gubi, et al., 2010):
Lack of suitable indoor maps
Limitations of the available technology
Indoor positioning implementations can be found in office buildings, warehouses, factories. Examples of indoor location sensing technologies include infrared positioning, indoor GPS-based systems, Ultra Wide Band (UWB), Wireless Local Area Network (WLAN), and of course RFID positioning. The dominant RF-based methods, however, are RFID and WLAN. In an indoor environment, localization of an object is possible through tracking in two or three dimensions. If tracking takes place in a 2-dimension space but on multiple planes, like tracking of people in a building of many floors, then the term 2.5-dimension can be used. The processing of the input data and the determination of the location can be implemented on a mobile device on the object or person being traced (client device), or on a central unit (server), the location of which can be, theoretically, anywhere in the world. The position can be reported either symbolically or in coordinates, which in turn can be absolute (e.g. longitude, altitude etc.) or relative (e.g. distance from a nearby point of reference).
2.3. Hybrid positioning systems
Hybrid positioning systems usually combine satellite location technology with ground-based systems in order to either increase the accuracy served by the satellite system or to provide location information for those areas where satellites cannot reach, such as indoors. The assisting technologies usually are mobile phone cell tower signals (GSM, LTE), WiFi, WiMAX, Bluetooth and others (AlterGeo, 2015).
RFID systems can be used to obtain indoors location information in such a way, that when combined with GNSS can create a system of seamless positioning. It is not mandatory for areas not covered by GNSS to be indoor areas; outdoors obstacles such as canopies can negatively affect satellite signal quality as well (Shikada, Shiraishi, & Takeuchi, 2012). An example of such a seamless system output is shown in Image 4. The light green points are obtained from the passive RFID system, while the red points from GPS. In (Shikada, Shiraishi, & Takeuchi, 2012), where this image was taken from, it shows a problem of overlapping data for the positions where both the RFID system and the GPS system feed the system with location data.
Hybrid positioning techniques can also be combining RFID and another technology (Bai, Wu, Wu, & Zhang, 2012), not necessarily aimed at indoor implementations only (Wen, 2010). Some examples of non-GNSS technologies that have been used to enhance the capabilities of RFID-based systems include ZigBee (a protocol used for Wireless Sensor Networks – WSN), Wi-Fi/WLAN, ultra wide band (UWB), infrared (IR), and ultrasonic. Such combinations can provide the user with increased accuracy and reliability.
3. RFID positioning
There are generally two kinds of implementing RFID-based location sensing: fixed tags–moving reader, which implies that the object or person whose position is to be estimated carries a reader, and fixed reader(s)–moving tags, where the setup is the opposite. In addition, that there are systems where readers and reference tags are stationary and a non-fixed tag is being tracked.
3.1. Fixed tags of known location – moving reader of unknown location
A two dimensional space can be interpreted as a Cartesian coordinate system, where each point that belongs to the said plane can be specified by a pair of numbers, usually (x, y). The implementation described below mainly consists of a grid of passive RFID tags equally distributed over the area where the positioning takes place and a portable RFID reader which is carried by the person/object being tracked. A system like the one illustrated below can achieve positioning accuracy of 50 cm if tag placement is 50 cm, the antenna is linearly polarized and the the RFID transmission power is 18dBm (Shiraishi, Komuro, Ueda, Kasai, & Tsuboi, 2008).
Each one of the installed tags has a unique ID number and its spatial location is known with a high accuracy. The spatial location and ID data are stored in a database; therefore, the memory requirement for the tags is low, as it only stores the ID number. The reader moves along the plane of tags at a distance, along the z axis.
As illustrated in image 5, the reader moves over the (x,y) plane at a distance d. The interrogation field of the reader reaches the tags on the floor. As it moves, several tags are read. Experimental applications have verified that the closer the reader to the tag plane is, the better the location estimation is. In addition, the reader detects significantly fewer tags if it is placed closer to the plane than the wavelength of the RFID system. For example, for tags operating at 950 MHZ, the wavelength, and therefore the minimum recommended distance from the tag plane, is about 0,30 m (Shiraishi, Komuro, Ueda, Kasai, & Tsuboi, 2008).
After receiving the tag IDs that have been detected, the position of the reader is estimated computationally. The easiest approach is to calculate the center of gravity of all the tags detected, but this method might not be the most appropriate because the reader might detect tags that are way off. Thus, a clustering approach can be more suitable. This method can locate clusters of tags and disregard outliers. For this purpose, GIS software that can perform spatial statistics operations can be used. In particular, the Hot Spot Analysis (Getis-Ord Gi*) can locate the clustering (Esri). There should be no reason to use global statistics first, which shall identify whether or not clustering exists, since it is taken for granted that there is a cluster of detected tags.
The reader in an implementation of a fixed grid of passive RFID tags can be small enough to be portable by blind people. An example is a prototype called SmartVision (Fernandes, Filipea, Costa, & Barroso, 2014), aimed at providing location-based services for the blind. The prototype is based on the same principle as the example above. There is a base layer of RFID tags which marks the area the people can move and several “layers” of points of interest (POI). Of course, the tags used still only store their ID information; whether a specific tag belongs to layers with POIs is associated in the GIS database. This way, people can navigate (or be informed about their position) on many levels, e.g. on a position marked as ‘safe’ (not wet, for example, since the database can be updated in real-time) and marked as ‘room 5’.
It is possible, however, to use RFID-based positioning system as above but for linear location sensing; along a path, for example. Active RFID systems operating at high frequencies (microwave) are already being used for electronic toll collection on motorways. Though they can be used (and are used) for tracking of vehicles and road traffic monitoring, they do not provide ‘position’ information in the sense of exact location of a vehicle on the road. Contrary to indoor positioning, the main challenge here is the moving speed of the tracked object (the car) and the selection of an RFID system with transponders able to communicate whilst moving at high speeds. In addition, the reader and the middleware installed on the car must be able to handle high data rate, given that many tags are read in a short time frame. Even if the tags only communicate their ID codes, the data adds up. In the experimentation setting of (Chon, Jun, Jung, & An, 2004), the reader moving at 165 km/h needs 81 cm of travel distance to read 128 bits of data from a stationary tag, therefore, its read range has to be at least 81 cm.
3.2. Fixed reader(s) & ref. tags of known location – moving tags of unknown location
The implementation opposite of §3.1 is to set readers to a fixed and known position and estimate the position of RFID tags within their interrogation field. The techniques used for this purpose are the ones presented in §1.3.
4. Conclusions & further study
Implementing an RFID-based system for positioning, especially indoor positioning, can have some advantages over competitive systems. RFID generally is a simple, flexible, portable and low-cost system that can provide identification, in a supply chain for example, and location information at the same time. However, the communication is one-way when dealing with passive tags and undesirable multipath effects are observed (Bai, Wu, Wu, & Zhang, 2012).
While the positioning techniques mentioned above are mainly focused on either 2D or 2.5D, the possibilities offered by a 3D positioning system are significant, especially in work sites. An example of such a location sensing system is by (Ko, 2013) and provides users with an alternative to the RFID-based symbolic tracking methods, such as the ones in a supply chain.
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