Evolutionary Optimization Based Set Joint Integrated Probabilistic Data Association Filter

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.

Automated summary

The joint integrated probabilistic data association (JIPDA) algorithm is widely used for automatic tracking of multiple targets. However, it has the well-known problem of track coalescence. In this study, a novel evolutionary optimization based joint integrated probabilistic data association (EOJIPDA) filter is developed to overcome this problem. By minimizing the trace of the covariance matrix, the accuracy of target state estimation can be improved.