Maximum likelihood estimation of transition probabilities using analytical center cutting plane method for unknown maneuvering emitter tracking by a wireless sensor network

Xiaomei Luo, Zhi Quan Luo, Kehu Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We consider the problem of unknown maneuvering emitter tracking by a wireless sensor network using the interacting multiple models (IMM) with the TDOA and FDOA measurements. Essential to this tracking framework is the Markov transition probability matrix (TPM) governing the jumps between multiple dynamic motion models for the maneuvering target. In practice, the TPM is unknown and has to be estimated. In this paper, we consider the maximum likelihood (ML) estimation of the TPM and propose a recursive algorithm to update the ML TPM estimate using the analytical center cutting plane method (ACCPM). Compared to the general batch ML method, the resulting recursive ML estimation method has a much lower per sample complexity. Simulation results show the efficacy of the proposed method with improved tracking performance.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2649-2652
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Convex optimization
  • EKF-IMM
  • ML estimation
  • Maneuvering emitter tracking
  • Markovian jump system

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