Research Proposal_NUS

Research ProposalShengyang LiJan 18, 2011◆TitlePositioning With RFID Technology◆AbstractRadio Frequency Identification (RFID) is a kind of positioning technology that uses radio waves to recognize and obtain the related information between the RFID reader and RFID tag. In the past few years, with the development of RFID technology, RFID equipment is frequently used in indoor and narrow space. For the indoor environment, positioning algorithm of high accuracy is extremely important due to the influence of serious multi-path and NLOS error in the indoor condition. Therefore, this paper mainly introduces UHF-RFID positioning technology in the indoor circumstance.As to the UHF-RFID indoor positioning, we prefer using improving Monte Carlo[1] positioning algorithm based on sampling to locate the indoor RFID tag and analyze the influence of ranging error, the number of samples and the tag stop while moving. The result shows that improving Monte Carlo positioning algorithm is less sensitive to the ranging error, thus it does not require much samples. Moreover, improving Monte Carlo positioning algorithm still can keep on tracing the location when TOA algorithm is invalid. In addition, we also analyze some relevant factors such as antenna polarization mismatch, signal noise, and construction material which may influence the RFID indoor location.◆IntroductionRadio Frequency Identification (RFID) is a newly non-contact automatic identification technology. RFID technology has been widely used in industry automation, commerce automation and transportation management such as automobile monitoring, finance trade, vehicle anti-theft system, and some other areas. With the increasing demands of industry practice, RFID location technology will be extraordinarily meaningful both on theory and practice.Typical RFID system is consisted of three parts, namely RFID tag, RFID reader anddata process unit (usually a computer or a server). We will mainly investigate indoor location through UHF-RFID system because its reading range is 4m to 6m. A famous RFID indoor location system is invented by Lionel M.Ni, called LANDMARC[2] which is consisted of many Active Tags and Readers. There are two components of the tag—one is Reference Active Tag (RAT) and the other is Positioned Active Tag (PAT) which attached on the object or person that needs to be located. UHF-RFID system puts RAT on the indoor known coordinates; then the Reader continuously records the energy value of the electromagnetic wave. The Reader will detect the energy value sent by PAT and compare it with the record value before the object with PAT gets in. Finally, the system will get the exact location of the object.Because of the influence of serious multi-path and NLOS error in the indoor condition, the measured value of TOA will deviate a lot. Therefore, many classical location algorithms such as Taylor(‘s) series[3], Least Square Method[4], Chan’s Algorithm[5] and Linear Location of Parallel (LLOP) are come up with to solve this problem. Based on these algorithms, we use polynomial fitting to make TOA curve smooth and eliminate random noise, and then we move the smooth curve to eliminate NLOS[6] error. Through these refinements, we can reduce the NLOS error to the least.The location algorithm based on TOA needs at least three antennas in one area, and the more antennas, the more RFID readers and higher costs are needed. But we cannot locate the tag when one of the three antennas loses its tag signal. But we could use Monte Carlo location algorithm to keep trace with the tag. However, traditional Monte Carlo location algorithm has two defects, one is hard to make real-time location, and the other is hard to locate the object stops while moving. Thus we improve the Monte Carlo location algorithm to make it suitable for the indoor location.Monte Carlo location algorithm actually is a recurrence Bayes Filter[8] to estimate the status of dynamic system. Improving Monte Carlo location algorithm builds predict model and static model to consummate traditional Monte Carlo location algorithm (on page 3). Here are the steps of the improving Monte Carlo location algorithm (the flow diagram also displayed afterward):<1> Initialization: put the initial location into database, the counter k=1.<2> Choose available Reader that can be detected the tag signal to locate<3> Calculate the probability of tag stops called p , if p is bigger than threshold value,jump to step 6, otherwise jump to step 4.<4> Use the improving Monte Carlo location algorithm to locate the tag.According to predict model, we can find out the most possible location of the tag, and that is the location of the tag.<5> Put the location of tag into database and jump to step 7.<6> Query database and the location of the tag when the time is k-1 is the location ofthe current location of the tag.<7> k=k+1, return to step 2.Improving Monte Carlo location algorithm is fit for dynamic environment; its stability and none-sensitivity to the number of samples are preferable characteristics for location. Furthermore, we can save computing storage and resource of simultaneously. Moreover, improving Monte Carlo location algorithm is still available when the TOA location algorithm is invalid and tag stops while moving. It is absolutely a practical method for the RFID positioning.◆Proposed ResearchThe proposed research follows three main directions:1. Method to expand the range in which RFID Reader could detect RFID Tag.For the range of detection, we could try to use Real Time Location System (RTLS) based on RFID location algorithm to get the exact location of the target. It is similar to GPS, but GPS is impacted seriously by the constructions. First, we use GPS to locate primarily and get the initial location of the target. Then, the Reader could trace the Tag quickly, and its accuracy could always reach to 1 meter. It is a very effective way that combines GPS with RTLS based on RFID to find out the Initialization k=1 Select available Reader Is Tag static or not? Monte Carlo positioning Put the location of Tag into database k=k+1Inquire database and find out the location of Tag when the time is k-1 Yes Noaccurate location of the target and expand the range of the detection.2.Provide the high speed positioning service for the enterprises to ensure thereal-time feature.The speed of locating the target is always a problem for the RFID real-time location. We could improve the speed of storage server in order to find out the data of client quickly; improving the algorithm to make calculation much faster is feasible; another way is using high-speed network such as 3G network to assist locating the target, which may popularize the use of positioning with RFID by ordinary consumers, finally resulting in fast searching of target.3.Solve the problem that the signal is reduced by the multi-path and NLOS error inthe indoor condition.In order to overcome the limitation of indoor condition, we could use the improving Monte Carlo algorithm to trace the target all the time since it is senseless to the blocks and not influenced much by the multi-path and NLOS error.◆Research Methods1.Read materials and thesis concerning with RFID location, including Real Timelocation System, Monte Carlo algorithm, network transfer, database inquiry, and electrical wave.2.Considering the characteristics of RFID location, design high-speed air-port thatcould be connected to the high-speed network so as to ensure the real-time location service, and use the sensitive chips that could detect tags in a large range.paring different algorithms to find out the pros and cons of them, thenchoose the one that suits to RFID location algorithm, or take advantages of some algorithms to design a more effective one, which has less influence on the RFID location.4.Figure out the factors that affect the transfer and accuracy of the RFID system.Analyze these factors and classify them, then decide which of them could be solved by coming up with an effective algorithm and which of them could be solved by improving the network and the device.◆References[1] Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2001). "Ch 5. Probabilistic Analysis and Randomized Algorithms". Introduction to Algorithms (2nd ed.). Boston: MIT Press and McGraw-Hill. ISBN 0-262-53196-8.[2] Abhishek P. Patil, Lionel M. Ni, Yunhao Liu, Yiu Cho Lau, LANDMARC: Indoor Location Sensing Using Active RFID, Proceedings of the First IEEE Conference on Pervasive Computing and Communications ( PerCom’03)[3] W. Foy, Position location solutions by Taylor series estimation [J], IEEE Trans. Aerospace Electron System, pp.187-193, 1976[4] Steven M. Kat, Fundamentals of Statistical Processing Volume 1: Estimation Theory [M], Publishing House of Electronics Industry. 2003[5] Timothy M. Chan. "Optimal output-sensitive convex hull algorithms in two and three dimensions". Discrete and Computational Geometry, Vol. 16, pp.361–368. 1996.[6] J. Caffery, A new approach to the geometry of TOA location [J], IEEE VTS Fall VTC20000, pp. 1943-1949, 2000。