Paper Info
Title
Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering Targets
Abstract
The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture (MBM). Considering that the single model is not enough to guarantee stable tracking performance for maneuvering targets, in this article, a multiple model PMBM (MM-PMBM) filter is proposed to cope with this problem. The proposed MM-PMBM filter extends the single-model PMBM filter recursion to multiple motion models by exploiting the jump Markov system (JMS). The performance of the proposed algorithm is examined from two scenarios with different detection probabilities. Moreover, the robustness of Markovian model transition probability matrices (TPMs) for the proposed MM-PMBM filter is also explored. The simulation results demonstrate that the proposed MM-PMBM filter performs well in terms of the tracking accuracy, including the target states and cardinality estimates, and also has good tolerance with respect to different TPMs.
Year
DOI
Venue
2019
10.1109/JSEN.2020.3022669
IEEE Sensors Journal
Keywords
DocType
Volume
Multiple model,poisson multi-Bernoulli mixture,maneuvering targets,jump markov system
Journal
21
Issue
ISSN
Citations 
3
1530-437X
2
PageRank 
References 
Authors
0.36
0
1
Name
Order
Citations
PageRank
Guchong Li171.79