Abstract
The joint integrated probabilistic data association (JIPDA) algorithm is widely used for the automatic tracking of multiple targets, but it has the well-known problem of track coalescence. By optimizing the posterior density, the accuracy of the target state estimation can be improved. Motivated by this idea, we developed a novel evolutionary optimization based joint integrated probabilistic data association (EOJIPDA) filter to overcome the coalescence problem of the JIPDA filter. The trace for the covariance matrix of the posterior density is used as the objective function for the above optimization problem. It is shown that the accuracy of the target state estimation can be improved by reducing the trace. Evolutionary optimization was employed to minimize the trace and optimize the posterior density. More specifically, we enumerated all the possible permutations of the targets and assign a unique index to each permutation. The resulting indices were randomly assigned to all possible association hypothesis events. Each assignment indicated one possible gene in the evolutionary algorithm. This process was repeated several times to arrive at the initial population. An illustrative example shows that the EOJIPDA filter can effectively improve the accuracy of state estimation. Numerical studies are presented for two challenging multi-target tracking scenarios with clutter and missed detections. The experimental results demonstrate that the EOJIPDA filter provides better tracking accuracy than traditional coalescence-avoiding methods.
Highlights
Multi-target tracking (MTT) is one of the most important low-level techniques used in radar, computer vision, Internet of things [1,2], and other surveillance systems [1,2,3,4,5]
We demonstrate the performance of the proposed EOJIPDA filter using two challenging MTT scenarios in the two-dimensional surveillance area
The joint integrated probabilistic data association (JIPDA) filter an effective method for MTT, but it suffers from the serious track coalescence problem
Summary
Multi-target tracking (MTT) is one of the most important low-level techniques used in radar, computer vision, Internet of things [1,2], and other surveillance systems [1,2,3,4,5]. The joint integrated probabilistic data association (JIPDA) filter uses the probability of target existence to measure the quality of the track and is effective for automatic tracking [9]. To overcome the track coalescence problem of the JIPDA filter and further improve the tracking accuracy, we combine the advantages of the RFS theory and the standard JIPDA filter and propose a novel filter, named evolutionary optimization based joint integrated probabilistic data association (EOJIPDA). The essential reason for the track coalescence is that the overlap of the tracking gates leads to the association uncertainty problem In this case, the posterior density becomes multimodal and the estimation of the multi-target state becomes less accurate.
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