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.
Bibliographical noteFunding Information:
This work was supported partially by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A1A1006101 ), partially by General Research Program through National Research Foundation of Korea funded by the Ministry of Education (Grant no. NRF-2013R1A1A2008698 ), partially by the Australian Research Council ( DP140102180 , LP140100471 , LE150100079 ), and the 111 Project (B12018) .
© 2015 Elsevier B.V.
- 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
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence