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Mobile Tracking in Mixed Line-of-Sight/Non-Line-of-Sight Conditions: Algorithm and Theoretical Lower Bound

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Abstract

The paper investigates the problem of mobile tracking in mixed line-of-sight (LOS)/non-line-of-sight (NLOS) conditions. The motion of mobile station is modeled by a dynamic white noise acceleration model, while the measurements are time of arrival (TOA). A first-order Markov model is employed to describe the dynamic transition of LOS/NLOS conditions. An improved Rao-Blackwellized particle filter (RBPF) is proposed, in which the LOS/NLOS sight conditions are estimated by particle filtering using the optimal trial distribution, and the mobile state is computed by applying approximated analytical methods. The theoretical error lower bound is further studied in the described problem. A new method is presented to compute the posterior Cramer-Rao lower bound (CRLB): the mobile state is first estimated by decentralized extended Kalman filter (EKF) method, then sigma point set and unscented transformation are applied to calculate Fisher information matrix (FIM). Simulation results show that the improved RBPF is more accurate than current methods, and its performance approaches to the theoretical bound.

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Author notes

  1. Liang Chen

    Present address: , Geodeetinrinne 2, P.O. Box 15, 02341, Masala, Finland

Authors and Affiliations

  1. Mathematics Department, Tampere University of Technology, Tampere, Finland

    Liang Chen, Simo Ali-Löytty & Robert Piché

  2. School of Information Science and Engineering, Southeast University, Nanjing, China

    Lenan Wu

Authors
  1. Liang Chen
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  2. Simo Ali-Löytty
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  3. Robert Piché
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  4. Lenan Wu
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Correspondence to Liang Chen.

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Chen, L., Ali-Löytty, S., Piché, R. et al. Mobile Tracking in Mixed Line-of-Sight/Non-Line-of-Sight Conditions: Algorithm and Theoretical Lower Bound. Wireless Pers Commun 65, 753–771 (2012). https://doi.org/10.1007/s11277-011-0294-7

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