Multi-target FIR tracking algorithm for Markov jump linear systems based on true-target decision-making
- Authors
- Lee, Chang Joo; Pak, Jung Min; Ahn, Choon Ki; Min, Kyung Min; Shi, Peng; Lim, Myo Taeg
- Issue Date
- 30-11월-2015
- Publisher
- ELSEVIER
- Keywords
- Multi-target tracking (MTT); Markov jump linear system (MJLS); Finite impulse response (FIR) filter; Multi-target FIR tracking algorithm (MTFTA); True-target decision making
- Citation
- NEUROCOMPUTING, v.168, pp.298 - 307
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEUROCOMPUTING
- Volume
- 168
- Start Page
- 298
- End Page
- 307
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/91862
- DOI
- 10.1016/j.neucom.2015.05.096
- ISSN
- 0925-2312
- Abstract
- Most existing multi-target tracking (MIT) 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 MU algorithm based on FIR filtering for Markov jump linear systems (MJISs). 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'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. (C) 2015 Elsevier B.V. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.