Abstract
This paper studies the problem of tracking a mobile device in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. NLOS error is assumed to be Gaussian with unknown mean and variance. An adaptive Rao-Blackwellized particle filter (RBPF) is proposed for mobile tracking in such scenarios. An extended Kalman filter is used to approximately estimate the mobile state, and the particle filter is applied to estimate the posterior distribution of sight conditions and the unknown static parameters, the distribution of which is updated by sufficient statistics. To improve the efficiency of the particle filtering, we use the approximate optimal proposal distribution for particle inference. Algorithm performance is investigated in the scenario of mobile tracking using signals of opportunity from digital TV (DTV) network. Simulation results show that the adaptive RBPF method is effective to infer the unknown NLOS parameter and can achieve good tracking accuracy using a small number of particles.
Highlights
Accurate and reliable positioning in non-line-of-sight (NLOS) conditions is a challenging task in many wireless positioning systems
An adaptive particle filtering method is proposed, which uses an extended Kalman filter (EKF) to approximately estimate the mobile state and applies the particle filter to estimate the posterior density of sight conditions and the unknown static parameters, the distribution of which is updated by sufficient statistics
6 Conclusions We have considered the problem of mobile tracking in the mixed LOS/NLOS conditions, where the statistical parameter of NLOS error is unknown
Summary
Accurate and reliable positioning in non-line-of-sight (NLOS) conditions is a challenging task in many wireless positioning systems. By considering a long period of observation, the distribution of NLOS range errors can be modeled by a positively biased Gaussian noise, while to be more reasonable, its mean and variance are assumed (static) unknown because the statistical parameters of the NLOS errors are highly dependent on the surroundings, and can not be known beforehand [12] Mobile tracking under such mixed LOS/NLOS conditions comes down to the problem of sequential state estimation with the inference of unknown static parameters. An adaptive particle filtering method is proposed, which uses an EKF to approximately estimate the mobile state and applies the particle filter to estimate the posterior density of sight conditions and the unknown static parameters, the distribution of which is updated by sufficient statistics. The paper is organized as follows: Section 2 presents the system model of mobile tracking in the mixed LOS/NLOS conditions; Section 3 formulates the problem within the Bayesian framework; in Section 4, the RBPF based adaptive mobile tracking method is described in detail; numerical results and performance comparison are presented and discussed in Section 5; and Section 6 draws some conclusions
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