Feature aided probabilistic data association for multi-target tracking

Feature aided tracking can often yield improved tracking performance over the standard radar tracking with positional measurements alone. However, the complexity of the tracker may dramatically increase due to the inclusion of the target feature state. In this paper, we study the situation where the target feature is a constant or slowly varying parameter with respect to the target state and can be observed together with the target position. We consider using such target feature data for data association which is a significant problem and dominates the outcomes of multi-target tracking in clutter. Extra target discrimination is obtained by computing a joint measurement likelihood which is typically used in a PDA framework. This idea is demonstrated via an example where the target down-range extent measurement is incorporated into a standard IPDA tracker to resolve closely spaced targets in clutter. A simple target extent model is therefore proposed. Our results indicate that when using the proposed feature aided data association process the complexity of data-to-track association can be greatly reduced. Moreover, the tracking performance of the IPDA tracker is greatly improved.