A RSS-EKF localization method using HMM-based LOS/NLOS channel identification

Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured RSSs as its observation. The parameters of HMM are obtained by an off-line training assuming that the LOS/NLOS can be identified during the training phase. With the HMM matrices, a forward-only algorithm can be utilized for real time sight conditions identification. The target is localized by extended Kalman Filter (EKF) by suitably combining with the sight conditions. Simulation results show that our proposed localization strategy can provide good identification to channel sight conditions, hence results in a better localization estimation.

[1]  Ismail Güvenç,et al.  NLOS Identification and Mitigation for UWB Localization Systems , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[2]  K. C. Ho,et al.  A new constrained weighted least squares algorithm for TDOA-based localization , 2013, Signal Process..

[3]  Leonard Barolli,et al.  A localization algorithm based on AOA for ad-hoc sensor networks , 2012, Mob. Inf. Syst..

[4]  Chin-Der Wann,et al.  NLOS mitigation with biased Kalman filters for range estimation in UWB systems , 2007, TENCON 2007 - 2007 IEEE Region 10 Conference.

[5]  Umberto Spagnolini,et al.  Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions , 2007, IEEE Transactions on Signal Processing.

[6]  Jiming Chen,et al.  Sensor network localization using kernel spectral regression , 2010, CMC 2010.

[7]  R. M. Buehrer,et al.  Non-line-of-sight identification in ultra-wideband systems based on received signal statistics , 2007 .

[8]  Naitong Zhang,et al.  NLOS Error Mitigation for UWB Ranging in Dense Multipath Environments , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[9]  Yan Zhang,et al.  Development of an integrated wireless sensor network micro-environmental monitoring system. , 2008, ISA transactions.

[10]  N.B. Mandayam,et al.  Decision theoretic framework for NLOS identification , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[11]  Y. Jay Guo,et al.  Statistical NLOS Identification Based on AOA, TOA, and Signal Strength , 2009, IEEE Transactions on Vehicular Technology.

[12]  David E. Culler,et al.  A practical evaluation of radio signal strength for ranging-based localization , 2007, MOCO.

[13]  Kaveh Pahlavan,et al.  Identification of the Absence of Direct Path in Indoor Localization Systems , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Jiming Chen,et al.  Wireless Sensor Networks Localization with Isomap , 2009, 2009 IEEE International Conference on Communications.

[15]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[16]  Andreas F. Molisch,et al.  Accurate Passive Location Estimation Using TOA Measurements , 2012, IEEE Transactions on Wireless Communications.

[17]  Pi-Chun Chen,et al.  A non-line-of-sight error mitigation algorithm in location estimation , 1999, WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466).

[18]  Brian D. O. Anderson,et al.  Wireless sensor network localization techniques , 2007, Comput. Networks.

[19]  Yuanwei Jing,et al.  An Indoor Mobile Localization Strategy for Robot in NLOS Environment , 2013, Int. J. Distributed Sens. Networks.