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

Jun Yan, Kegen Yu, Lenan Wu

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number105
Pages (from-to)1-16
Number of pages16
JournalEurasip Journal on Advances in Signal Processing
Volume2014
Issue number1
DOIs
Publication statusPublished - 10 Jul 2014
Externally publishedYes

Keywords

  • Fuzzy modeling
  • Kalman filter
  • Maximum likelihood estimator
  • Non-line-of-sight
  • Probability-possibility transformation
  • Target tracking

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