Adaptive mobile tracking in unknown non-line-of-sight conditions with application to digital TV networks

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.

• Publication year

  2014

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  2014-02-21

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1186/1687-6180-2014-22
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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