期刊
SENSORS
卷 22, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/s22134759
关键词
component existence probabilities; false-track discrimination; multi-maneuvering-targets; smoothing; target existence probabilities
资金
- National Research Foundation of Korea (NRF) [2022R1G1A101094611]
This research extends the FIsJITS filter to address the unknown dynamics of multi-maneuvering targets (MMT) in tracking scenarios, and proposes the MMT-sJITS method. By optimizing the estimation of target measurements concealed by joint measurements, this method reduces computational complexity and improves tracking performance.
This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
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