Abstract
The likelihood determined by the distance between measurements and predicted states of targets is widely used in many filters for data association. However, if the actual motion model of targets is not coincided with the preset dynamic motion model, this criterion will lead to poor performance when close-space targets are tracked. For rigid target tracking task, the structure of rigid targets can be exploited to improve the data association performance. In this paper, the structure of the rigid target is represented as a hypergraph, and the problem of data association is formulated as a hypergraph matching problem. However, the performance of hypergraph matching degrades if there are missed detections and clutter. To overcome this limitation, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than the distance between predicted states and measurements. Consequently, the probability of each target associated with each measurement (joint association probabilities) can be obtained. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, because all undetected cases is considered, proposed approach is usable in real time only for a limited number of targets. Extensive simulations have demonstrated the significant performance improvement of our proposed method.
Highlights
For multi-target tracking (MTT) task, the number and the states of targets are jointly estimated from a sequence of measurements
In JPHGM-labeled multi-Bernoulli filter (LMB), the likelihood is built based on group structure rather than distance between predicted states and measurements
The computational complexity of the proposed approach exponentially grows with the number of targets since all undetected cases are considered, the proposed approach is usable in real time only for a limited number of targets
Summary
For multi-target tracking (MTT) task, the number and the states of targets are jointly estimated from a sequence of measurements. Joint probabilistic data association (JPDA) filter [7,8], multiple hypotheses tracking (MHT) [9,10], and random finite set (RFS) framework [11,12] are three major works for MTT. The structure information is beneficial to MTT, the targets in group are treated independently in JPDA filter, MHT, LMB, etc. In these filters, the measurement is associated with the closest target ’s state, the reason is that the smallest distance corresponds to the highest likelihood under the assumption that these targets share common motion. In [31], the hypergraph matching labeled multi-Bernoulli (HGM-LMB) filter is proposed to enhance the performance of data association.
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