Distributed multiple-model estimation for simultaneous localization and tracking with NLOS mitigation

Wenling Li, Yingmin Jia, Junping Du, Jun Zhang

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

This paper studies the problem of simultaneous localization and tracking (SLAT) in non-line-of-sight (NLOS) environments. By combining a target state and a sensor node location into an augmented vector, a nonlinear system with two jumping parameters is formulated in which two independent Markov chains are used to describe the switching of the target maneuvers and the transition of LOS/NLOS, respectively. To derive the state estimate of the proposed jump Markov nonlinear system for each sensor node, an interacting multiple-model (IMM) approach and a cubature Kalman filter (CKF) are employed. As the number of mode-conditioned filters exponentially grows with the increases in the number of active sensor nodes in the centralized fusion, a distributed scheme is adopted to reduce the computational burden, and a covariance intersection (CI) method is used to fuse sensor-based target-state estimates. A numerical example is provided, involving tracking a maneuvering target by a set of sensors, and simulation results show that the proposed filter can track the target and can estimate the positions of active sensor nodes accurately.

Original languageEnglish
Article number6461426
Pages (from-to)2824-2830
Number of pages7
JournalIEEE Transactions on Vehicular Technology
Volume62
Issue number6
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Distributed estimation
  • jump Markov nonlinear system
  • simultaneous localization and tracking (SLAT)

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