Fuzzy modeling, maximum likelihood estimation, and Kalman filtering for target tracking in NLOS scenarios

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• 作者：Jun Yan (1)
  Kegen Yu (2)
  Lenan Wu (3)
  
  1. College of Telecommunications and Information Engineering ; Nanjing University of Posts and Telecommunications ; Nanjing ; 210003 ; China
  2. School of Geodesy and Geomatics ; Wuhan University ; Wuhan ; 430072 ; China
  3. School of Information Science and Engineering ; Southeast University ; Nanjing ; 210096 ; China
  
• 关键词：Fuzzy modeling ; Probability ; possibility transformation ; Non ; line ; of ; sight ; Maximum likelihood estimator ; Kalman filter ; Target tracking
• 刊名：EURASIP Journal on Advances in Signal Processing
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：2014
• 期：1
• 全文大小：1,976 KB
• 参考文献：1. Yu, K, Sharp, I, Guo, YJ (2009) Ground-Based Wireless Positioning. Wiley-IEEE, Hoboken CrossRef
  2. Gezici, S, Poor, HV (2009) Position estimation via ultra-wide-band signals. Proc. IEEE 97: pp. 386-403 CrossRef
  3. Ahonen, S, Eskelinen, P (2003) Mobile terminal location for UMTS. IEEE Aerosp. Electron. Syst. Mag 18: pp. 23-27 CrossRef
  4. Anisetti, M, Ardagna, CA, Bellandi, V, Damiani, E (2011) Map-based location and tracking in multipath outdoor mobile networks. IEEE Trans. Wireless Commun 10: pp. 814-824 CrossRef
  5. Yu, K, Guo, YJ (2008) Improved positioning algorithm for nonline-of-sight environments. IEEE Trans. Vehicular Technol 57: pp. 2342-2353 CrossRef
  6. Kim, W, Lee, JG, Jee, G-I (2006) The interior-point method for an optimal treatment of bias in trilateration location. IEEE Trans. Vehicular Technol 55: pp. 1291-1301 CrossRef
  7. Miao, H, Yu, K, Juntti, M (2007) Positioning for NLOS propagation: algorithm derivations and Cramer-Rao bounds. IEEE Trans. Vehicular Technol 56: pp. 2568-2580 CrossRef
  8. Wang, X, Wang, Z, ODea, B (2003) A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. IEEE Trans. Vehicular Technol 52: pp. 112-116 CrossRef
  9. Yu, K, Dutkiewicz, E (2013) NLOS identification and mitigation for mobile tracking. IEEE Trans. Aerosp. Electron. Syst 49: pp. 1438-1452 CrossRef
  10. Cong, L, Zhuang, W (2005) Nonline-of-sight error mitigation in mobile location. IEEE Trans. Wireless Commun 4: pp. 560-573 CrossRef
  11. Wang, X, Fu, M, Zhang, H (2012) Target tracking in wireless sensor networks based on the combination of KF and MLE using distance measurements. IEEE Trans. Mobile Comput 11: pp. 567-576 CrossRef
  12. Le BL, Ahmed K, Tsuji H: Mobile location estimator with NLOS mitigation using Kalman filtering. New Orleans, 16鈥?0 March 2003. / Proceedings of Wireless Communications and Networking Conference1969鈥?973.
  13. Zaidi, ZR, Mark, BL (2005) Real-time mobility tracking algorithms for cellular networks based on Kalman filtering. IEEE Trans. Mobile Comput 4: pp. 195-208 CrossRef
  14. Yu, K, Dutkiewicz, E (2012) Geometry and motion based positioning algorithms for mobile tracking in NLOS environments. IEEE Trans. Mobile Comput 11: pp. 254-263 CrossRef
  15. Mazuelas, S, Lago, FA, Fernandez, P, Bahillo, A, Blas, J, Lorenzo, RM, Abril, EJ (2010) Ranking of TOA measurements based on the estimate of the NLOS propagation contribution in a wireless location system. Wireless Pers. Commun 53: pp. 35-52 CrossRef
  16. Liao, JF, Chen, BS (2006) Robust mobile location estimator with NLOS mitigation using interacting multiple model algorithm. IEEE Trans. Wireless Commun 5: pp. 3002-3006 CrossRef
  17. Morelli, C, Nicoli, M, Rampa, V, Spagnolini, U (2007) Hidden Markov models for radio localization in mixed LOS/NLOS conditions. IEEE Trans. Signal Process 55: pp. 1525-1542 CrossRef
  18. Hammes, U, Zoubir, AM (2011) Robust MT tracking based on M-estimation and interacting multiple model algorithm. IEEE Trans. Signal Process 59: pp. 3398-3409 CrossRef
  19. Mcguire, M, Plataniotis, KN (2003) Dynamic model-based filtering for mobile terminal location estimation. IEEE Trans. Vehicular Technol 52: pp. 1012-1031 CrossRef
  20. Huerta, JM, Vidal, J, Giremus, A, Tourneret, J-Y (2009) Joint particle filter and UKF position tracking in severe non-line-of-sight situations. IEEE J. Selected Topics Signal Process 3: pp. 874-888 CrossRef
  21. Mauris, G, Lasserre, V, Foulloy, L (2000) Fuzzy modeling of measurement data acquired from physical sensors. IEEE Trans. Instrum. Meas 49: pp. 1201-1205 CrossRef
  22. Chen, L, Ali-Loytty, S, Piche, R, Wu, LN (2012) Mobile tracking in mixed line-of-sight/non-line-of-sight conditions: algorithm and theoretical lower bound. Wireless Pers. Commun 65: pp. 753-771 CrossRef
  23. Chen, L, Wu, LN (2009) Mobile positioning in mixed LOS/NLOS conditions using modified EKF banks and data fusion method. IEICE Trans. Commun E92-B: pp. 1318-1325 CrossRef
  24. Bar-Shalom, Y, Li, XR, Kirubarajan, T (2001) Estimation with Applications to Tracking and Navigation, Theory Algorithms and Software. Wiley, New York CrossRef
  25. Silventoinen MI, Rantalainen T: Mobile station emergency locating in GSM. India, 1996. / IEEE International Conference on Personal Wireless Communications232鈥?38.
  26. Caffery JJ: A new approach to the geometry of TOA location. Boston, MA, 24鈥?8 September 2000. / the 52th IEEE Vehicular Technology Conference (VTC-Fall) 4:1943鈥?949.
  27. Guvenc I, Gezici S, Watanabe F, Inamura H: Enhancements to linear least squares localization through reference selection and ML estimation. Las Vegas, 31 March to 3 April 2008. / IEEE Wireless Communications and Networking Conference (WCNC)284鈥?89.
  28. Chen PC: A non-line-of-sight error mitigation algorithm in location estimation. New Orleans, 21鈥?4 September 1999. / IEEE Wireless Communications and Networking Conference (WCNC) 1:316鈥?20.
  29. Kay, SM (1998) Fundamentals of Statistical Signal Processing Volume II: Detection Theory. Prentice Hall, New Jersey
  
• 刊物主题：Signal, Image and Speech Processing;
• 出版者：Springer International Publishing
• ISSN：1687-6180

文摘

To mitigate the non-line-of-sight (NLOS) effect, a three-step positioning approach is proposed in this article for target tracking. The possibility of each distance measurement under line-of-sight condition is first obtained by applying the truncated triangular probability-possibility transformation associated with fuzzy modeling. Based on the calculated possibilities, the measurements are utilized to obtain intermediate position estimates using the maximum likelihood estimation (MLE), according to identified measurement condition. These intermediate position estimates are then filtered using a linear Kalman filter (KF) to produce the final target position estimates. The target motion information and statistical characteristics of the MLE results are employed in updating the KF parameters. The KF position prediction is exploited for MLE parameter initialization and distance measurement selection. Simulation results demonstrate that the proposed approach outperforms the existing algorithms in the presence of unknown NLOS propagation conditions and achieves a performance close to that when propagation conditions are perfectly known.