Multi-target FIR tracking algorithm for Markov jump linear systems based on true-target decision-making

Chang Joo Lee, Jung Min Pak, Choon Ki Ahn, Kyung Min Min, Peng Shi, Myo Taeg Lim

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Most existing multi-target tracking (MTT) algorithms are based on Kalman filters (KFs). However, KFs exhibit poor estimation performance or even diverge when system models have parameter uncertainties. To overcome this drawback, finite impulse response (FIR) filters have been studied; these are more robust against model uncertainty than KFs. In this paper, we propose a novel MTT algorithm based on FIR filtering for Markov jump linear systems (MJLSs). The proposed algorithm is called the multi-target FIR tracking algorithm (MTFTA). The MTFTA is based on the decision-making process to identify the true-target[U+05F3]s state among candidate states. The true-target decision-making process utilizes the likelihood function and the Mahalanobis distance. We show that the proposed MTFTA exhibits better robustness against model parameter uncertainties than the conventional KF-based algorithm.

Original languageEnglish
Pages (from-to)298-307
Number of pages10
JournalNeurocomputing
Volume168
DOIs
Publication statusPublished - 2015 Nov 30

Keywords

  • Finite impulse response (FIR) filter
  • Markov jump linear system (MJLS)
  • Multi-target FIR tracking algorithm (MTFTA)
  • Multi-target tracking (MTT)
  • True-target decision making

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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