A Study of Indoor Localization Techniques Octavian - Modest MANU Ştefan cel Mare University octavianm@stud.usv.ro Abstract: This paper will present some of the actual indoor localization techniques. In the following, some of the localization techniques will be analyzed such as: GPS localization, Infrared Localization, Ultrasonic localization, GSM fingerprint localization, Döppler effect radio localization, RFID localization, W-LAN localization and location estimates in Wireless Sensor Networks. The advantages and disadvantages of each presented location method will be discussed. Keywords: GSM, GPS, Indoor localization, Infrared, ISM Band, RFID, Sensor Networks, Ultrasound. 1. Introduction to Indoor Localization The location of people and objects relative to their environment is a crucial piece of information. Indoor localization has found many applications besides the usual asset tracking and security. The traditional applications of indoor location might be as simple as tracking a valuable shipping box or detecting the theft of a laptop computer, or as complex as helping someone to find his or her way around an unfamiliar building (e.g., museums or art galleries). Another use of indoor localization is smart sensor networks where data recorded from a wireless sensor is correlated to a position, for example the temperature recorded of a certain component of a machine or in a given refrigeration room. Hospitals and day care centers have started using positioning technologies to locate personnel or the nearest doctor (or medical equipment) in case of an emergency. Some use of indoor localization could be done in Shopping assistance and Follow-me services. Games may also get use of indoor localization technology. 2. Indoor Localization Methods There are several methods for implementing indoor localization, some with better results than others. In the following, an analysis of some indoor localization techniques will be made, and the potential of each method will be discussed. 2.1. GPS Localization The U.S. Department of Defense (DoD) began developing in the late 1960s a satellitebased localization system for military purposes, which eventually evolved into the Global Positioning System (GPS). Fig. 1. A NAVSTAR GPS satellite on public display at the San Diego Aerospace Museum. As of March 2008 the system consists of a constellation of 31 actively broadcasting small satellites and two kept as spares [1]. In Figure 1 is presented one of the satellites. They are placed in a non-uniform arrangement, which provide an improvement in reliability and availability of the system in the event of multiple satellites breakdown. The active satellites broadcast precise time signals on two frequencies L1 = 1575.4 MHz and L2 = 1227.6 MHz. Civil GPS receivers use band L1 while, the band L2 is reserved for PPS (Precision Positioning Service) - mainly military GPS receivers. The transmitted data on both frequencies are modulated onto the carrier
signal by phase modulation. The L1 carrier is modulated with C/A (Coarse/Acquisition) code and P(Y) (Precise code) code, while the L2 carrier is modulated only with P(Y) code. The C/A code is a pseudorandom code clearly defined for each satellite, having 1.023 million chips per second. The P(Y) code has 10.23 million chips per seconds and it is encrypted. The development of other technologies such as Differential GPS, allows even civilian GPS units to obtain accuracy greater than 10 meters. For specialized applications like surveying, the technology allows accurate measurements at the centimeter level. The radiated power of the signal transmitted by the satellites is only about 50 W. For comparison a television satellite irradiates 100 W focused on a single region, and for the reception it is necessary a dish antenna. Although modern GPS receivers, using advanced signal processing techniques, are able to receive signals of weaker than -160 dbm, the signal from the GPS satellites is still too weak to penetrate most buildings. The advantages of GPS localization arise from the fact that this technology can be freely used and with minimal cost on infrastructure. The downfall of this localization techniques is that even with the use of assisted GPS (A-GPS) - a technique which utilizes cellular and data networks in order to connect to an assistance server to obtain more precise data - the accuracy of GPS technology is not sufficient enough for indoor localization applications. 2.2. Infrared Localization Infrared Localization is a method to determine localization of objects or people by using various infrared emitters and receivers. The localization method using Modulated Infrared (IR) technology provide advantages such as confinement of the signals inside the room (IR does not penetrate through walls) and the absence of radio electromagnetic interference. In addition the power of transmitted IR signal can be easily adjusted to cover only the area of interest. Tom Pfeifer and Dirk Elias [2] developed an indoor localization application combining infrared, radio and IP networks. The system proposed by them uses IR ID beacons as notnetworked tags that periodically send their ID to IR receivers located across room. A large number of small ID Beacon devices are placed at various fixed position along the rooms. Each of them emits a unique serial number. The signal strength of the ID beacon can be modified, thus adjusting the maximum transmission distance. A small device in the form of a badge for example, worn by personnel receives the unique IR signal transmitted by IR beacons. When a new IR ID from a beacon has been received, the badge determines the location based on the received ID and transmits a packet via a radiofrequency channel to the Smart IP bridge. The packet consists of a unique badge ID and the last received location ID. The Smart IP bridge receives the packets from the RF badges and stores them into a database or can send the information further via Ethernet interface, or other communication methods. Fig. 2. Component Architecture of an Infrared Localization System [2]. In Figure 2 it is presented the system architecture implemented by [2]. The system described here has the advantage of being cheaper to implement than other IR systems. But, the deployment of such as system is not easily made. Furthermore, a badge concealed from visibility (in a pocket, or just out of proper reception of the sensor) could not offer any more position information to its owner. 2.3. Ultrasonic Localization Ultrasonic localization system use ultrasonic waves to measure distance between fixed point station and the mobile system whose position is being determinate. To implement such a system there are needed multiple ultrasonic receivers. Synchronizing receivers is done via IR or radio waves, because the speed of radio waves is much greater than the speed of ultrasonic waves. The transmitter sends at the same time a
radio signal and an ultrasonic wave. Radio signal reaches receivers almost instantaneous, giving them the synchronization signal. Receivers start to measure time between synchronization signal and the detection of ultrasonic waves, and then calculate the distance between transmitter and them. Do-Eun Kim et al. [3] developed an ultrasonic indoor localization of a robot using only one transmitter and two receivers with an error of no more than 2 cm. In Figure 3 the communication packets and the necessary time are presented. The RF packets transmitted time and the arrival time of the sound wave (which travel with 340 m/s) covering a distance of 5m was added up to require 40 ms. Adding the time of bidirectional RF communication the total time necessary for location is 70 ms. Thus, position updates could be done approximate 13 times a second. transmitting on the same frequency (e.g., microwaves, cordless phones); and (v) the significant expense and complexity of cellular base stations results in a network that evolves slowly and is only reconfigured infrequently. Veljo Otsason et al. [4] propose an idea that makes accurate GSM-based indoor localization possible by using wide signal-strength fingerprints. They compared the stability of GSM and 802.11 signals by measuring received signal strength from nearby access points and 6 strongest GSM cells at various time intervals. Fig. 3 shows a 3-hour segment of the above measurement. Fig. 4. The stability of 802.11 and GSM signals over time [4]. Fig. 3. Communication packets and time in the system proposed [3]. Advantages of this localization technique are the relative low cost and ease of implementing. Disadvantages of an ultrasonic localization system arise from the multipath reception that could disturb measurements of the distance between emitter and receivers, and the complexity of a large scale implementation. 2.4. GSM Localization GSM-based indoor localization has several benefits: (i) GSM coverage outreaches the coverage of 802.11 networks; (ii) the wide acceptance of cellular phones A localization system based on cellular signals, such as GSM, leverages the phone s existing hardware and removes the need for additional radio interfaces; (iii) because cellular towers are dispersed across the covered area, a cellular-based localization system would still work in situations where a building s electrical infrastructure has failed and the cellular systems are designed to tolerate power failures. GSM, unlike ISM band, operates in a licensed band, and therefore does not suffer from interference from nearby devices The wide fingerprint methods includes the measuring the RSS of 6-strongest GSM cells and readings of up to 29 additional GSM channels, most of which are strong enough to be detected, but too weak to be used for efficient communication. The results show that the proposed GSMbased indoor localization system can effectively differentiate between floors and achieves median within-floor accuracy as low as 2.5 meters. The main disadvantage of GSM based localization is the small number of Base Transceiver Stations (BTS) located in large countryside zones. A GSM localization system used in this area will receive a small number of channels making localization more difficult. 2.5. Döppler Effect Radio Localization This localization system analyzes Döppler frequency shifts of radiofrequency signals from spinning beacons, which are then used to calculate orientation angles to a target. By obtaining orientation angles from two or more beacons, the system can precisely locate stationary or slow-moving targets. Ho-lin Chang et al. [5] developed a system using spinning
beacons to create and detect predictable and highly distinguishable Döppler signals for submeter localization accuracy. RFID tags and readers communicate via magnetic coupling, which limits the reading range to less than 10 cm for typical readers, using simple loop antenna. Because of the wavelength of approximately 22 m these systems operate in the near field with the magnetic field strength dropping as one over the distance cubed. For higher frequencies, such as 400 MHz and 900 MHz, wavelengths are about 0.3 0.8 m. These systems operate in the far field. Coupling is done via the electric field and the field drops more slowly (as one over the distance). This results in an anticipated read range of several meters for passive tags. Fig. 5. The observed Döppler shifted frequencies [5]. They implementation relied on the spinning motions of selective infrastructure nodes to produce predictable, distinguishable Döppler signals for high-precision localization. A beacon embedded with a RF transceiver module transmits radio signals while spinning. However, a typical RF carrier frequency in the 400 MHz ~ 2.4 GHz range is too high for analysis by a hardware-constrained sensor node due to its slow clock and limited sampling rate. The radio interferometry method is therefore used to overcome such limitations. Radio interferometry measures RF Döppler shift with sufficient accuracy using inexpensive sensor hardware. The experimental results revealed a median error of 40~50 centimeters and a 90% error of 70~90 centimeters. In Figure 5 the frequency shift from the Döppler Effect is visible, comparing two signals measured by two nodes, one reference node (R) and one target node (X). The major problems of this approach are: relative large system complexity, time synchronization error, carrier frequency drift and indoor multipath interference. Döppler Effect causes the direct line-of-sight and reflected multipath signal to be at slightly different frequencies. 2.6. RFID Localization RFID (radio-frequency identification) devices are attached to persons or to moveable objects so that the persons or objects can be tracked using fixed readers (special-purpose radios) at different locations. L. E. Miller et al. [6] propose a system for indoor localization using RFID tags/readers and inertial sensors such as accelerometers and gyroscopes. The 13.56 MHz Fig. 6. RFID tags attached to walls [9]. It has been observed that 600 MHz 2 GHz is the best frequency band for propagation in buildings. The passive 900 MHz RFID tags can be read with hand held reader from a distance of approximately 3 meters. A large number of RFID tags which contain location information can be deployed to cover an entire building, like those on Figure 6. A person with a hand held reader could read the closest tag and obtain information about his or her position. The disadvantage of this implementation is the large number of RFID tags which need to be used, and prerecorded with location information. 2.7. Localization Using the ISM Band The 2.4 GHz ISM (Industrial, Scientific and Medical) band has been shared with license-free error-tolerant communications applications such as wireless LANs (IEE 802.11), Bluetooth (IEEE 802.15.1), ZigBee (IEEE 802.15.4) and others. There are two fundamental merits in using IEEE 802.11 localization schemes: for one, the hardware is cost effective and easy to install; secondly, there are many existing installations of
IEEE 802.11. Dan J. Kim et al. [7] observed that Bluetooth is less susceptible of interference from W-LAN than the other way round. Usually to estimate a location, trilateration is performed using one of this information [8]: time (TOA Time of Arrival, TDOA Time Difference of Arrival) and information regarding power of electromagnetic wave (RSS Received Signal Strength). In the first case, by assuming EM wave traveling at the speed of light and by acquiring the time of flight, we can estimate a circle of presence of the transmitter, and by using the intersection of three or more circles (i.e. transmitters); we can get a position of the user. With the absence of line of sight, the time delay will no longer represent the time consumed by direct flight, and thus the evaluation of distance is erroneous. In the case of power, the theoretical concept relies on power loss due to travel, therefore by calculating the received power and by comparing it with the transmitted power, the total dissipated power can be estimated, and assuming that this power is due to path-loss, it is consequently proportional to the distance. The absence of line of sight and more importantly multipath diversity and the numerous reflections due to the irregularities in the architecture makes the power loss information inaccurate, which draws high errors. Kevin D Hoe et al. [10] proposed an indoor localization estimation system based on a wireless sensor network, using the communication standard ZigBee. They used for location estimation the RSS information of the received signal. One of the multipath effects is the fast fading. In Figure 7 the measured values of the RSS between two basic RF nodes are presented. The distance between nodes is increased from one to two meters, with a step of 5 cm. Fig. 7. Fast fading effect [10]. Another aspect of multipath interferences can be observed in Figure 8. In this figure, the received signal strength measured between two static nodes was presented. The measurements were made over a period of 300 seconds. Fig. 8. Signal strength over time between two static nodes [10]. To overcome these impediments an entirely different approach emerged, the fingerprinting localization. Fingerprinting localization technique is based on the concept of identifying specified position by relying on some data that can represent a location. The fingerprint needs to be unique for each location and it also need to be reproducible. There is a major difference relative to the classical methods; in this case, the information is directly related to the position and not to a mathematical representation of the propagation behavior of the signal. 3. Conclusion Although there are many indoor localization techniques, none of these methods is the most efficient and precise. Each of the previously presented methods has its own advantages and disadvantages. The method of localization using GPS signal can be used only in small buildings with only one floor, because radio waves from GPS satellites do not penetrate multiple walls or floors. The method of localization implying the use of infrared or ultrasound it is difficult to implement, and the multipath errors reduces drastically the localization precision. The GSM fingerprint methods give the best result in dense urban locations, where a large number of BTS are received. In the countryside, where only a few couples of BTS cover a large area, this method will be less useful. Döppler effect localization relies on complex hardware with moving parts which are expensive and high power demanding. RFID localization requires the
use of multiple RFID tags placed at various locations in order to map the localization area. W-LAN localization is affected by multipath and fast fading effects. The fingerprint method relies on a previously traced radio map of the localization zone. One possible solution for implementing the indoor radio localization techniques can be the use of triangulation method based on the measurements of the Angle of Arrival (AoA). This can be done by the use of antenna array in order to steer a focused beam of radiation, thus avoiding some of the multipath interferences. Further research needs to be done in this field. References [1 GPS INFO, Naval Oceanography portal http://www.usno.navy.mil/usno/time/gps/gpsinfo. [2] T. Pfeifer and D. Elias, Commercial Hybrid IR/RF Local Positioning System, Kommunikation in Verteilten Systemen, University of Leipzig, Germany Feb 26-28, 2003. [3] D. Kim, K.-H. Hwang, D.-H. Lee, T.-Y. Kuc, A Simple Ultrasonic GPS System for Indoor Mobile Robot System using Kalman Filtering, robogames, 2007. [4] Veljo Otsason, et al. "Accurate GSM Indoor Localization", UbiComp 2005, LNCS 3660, pp. 141 158. [5] H.-l. Chang et al. Spinning Beacons for Precise Indoor Localization, NTU UbiComp - National Taiwan University. [6] L. E. Miller, P. F. Wilson et al. "RFID-Assisted Indoor Localization and Communication for First Responders", National Institute of Standards and Technology, ISART 2006. [7] Dan J. Kim, Abhishek P. Patil, Lionel M. Ni, A study of frequency interference and indoor location sensing with 802.11b and Bluetooth technologies, Int. J. Mobile Communications, Vol. 4, No. 6, 2006. [8] A. Taok, N. Kandil, S. Affes, Neural Networks for an indoor localization fingerprinting Using Ultra- Wideband, 2nd International Conference on Wireless Communications in Underground and Confined Areas (ICWCUCA), Val-d'Or, Québec, Canada, Aug. 25-27, 2008. [9] M. Philipose, K. P. Fishkin, Mapping and Localization with RFID Technology, Intel Research Seattle, December 2003 http://www.seattle.intelresearch.net/pubs/012020041250_211.pdf [10] K. D'hoe, G. Ottoy, J.-P. Goemaere and L. De Strycker, "Indoor Room Location Estimation", Advances in Electrical and Computer Engineering Journal, Vol. 8, No 2, pp. 78 81, 2008. Octavian Modest MANU Ph.D. Student at Ştefan cel Mare University of Suceava, Degree in System Hardware Engineering of and Computer Science at the Faculty of Electrical Engineering and Computer Science, PhD student in Electronic Engineering and Telecommunications, PhD supervisor: prof. eng. Adrian GRAUR, PhD.