A Nonconvex Cost Optimization Approach to Tracking Multiple Targets by a Parallel Computational Network

Abstract

The problem of tracking multiple targets in the presence of displacement noise and clutter is formulated as a nonconvex optimization problem. The form of the suggested cost function is shown to be suitable for the Graduated Non-Convexity algorithm, which can be viewed as deterministic annealing. The method is first derived for the two-dimensional (spatial/ temporal) case, and then generalized to the multi-dimensional case. The complexity grows linearly with the number of targets. Computer simulations show the performance with crossing trajectories.

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