Abstract
This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.
Highlights
Multiple-target tracking (MTT) [1] estimates the number and states of moving targets based on the sensor observations
Erefore, the random finite set (RFS) [9, 10] has received much attention in the MTT domain for the merit of avoiding explicit data association steps. is theory leads to the development of various MTT algorithms, including the probability hypothesis density (PHD) [11], cardinalized PHD (CPHD) [12], arithmetic average multi-Bernoulli (AAMB) [13], and multisensor multitarget multi-Bernoulli (MSMeMBer) [14] filters
As the extended-target PHD (ET-PHD) filter can only estimate the centroid state of the extended target, the PHD filters with shape estimation were proposed in [17, 18]
Summary
Multiple-target tracking (MTT) [1] estimates the number and states of moving targets based on the sensor observations. The extended-target MSMeMBer (ET-MS-MeMBer) filter and its Gaussian inverse Wishart (GIW) mixture implementation was proposed in [24]. E ET-MS-MeMBer filter assumes that the observation of extended targets obeys the approximate Poisson-Body (APB) model [25], and the update process of the MS-MeMBer filter is modified This method ignores the constraint of the clutter measurement set in the multisensor measurement partitioning process, which may degenerate the quality of the multisensor measurement partition and, the filtering performance of the MS-MeMBer filter in high-clutter-density scenarios. Simulation results show that compared to the ETMS-MeMBer filter, the proposed filter has higher accuracy in estimating the target cardinality and target state under high-clutter-density conditions.
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