Journal
IEEE SENSORS JOURNAL
Volume 21, Issue 3, Pages 3143-3154Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3022669
Keywords
Radio frequency; Target tracking; Sensors; Uncertainty; Probability density function; Markov processes; Multiple model; poisson multi-Bernoulli mixture; maneuvering targets; jump markov system
Funding
- National Natural Science Foundation of China [61771110, 61801085]
- Chang Jiang Scholars Program
- 111 Project [B17008]
- Chinese Postdoctoral Science Foundation [2019T120825, 2018M633352]
- GF Science and Technology Special Innovation Zone Project
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In this article, a multiple model PMBM (MM-PMBM) filter is proposed to address the tracking stability issue for maneuvering targets. The filter extends the single-model PMBM filter recursion to multiple motion models by utilizing the jump Markov system (JMS). Simulation results show that the MM-PMBM filter performs well in terms of tracking accuracy and is robust to different Markovian model transition probability matrices (TPMs).
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.
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