A multiple model framework for image-enhanced tracking of maneuvering targets

We consider tracking algorithms for maneuvering targets when the observations include extra information on the current operating mode of the target obtained from an image sensor. The target is modelled as a Markov jump linear system and the image-based observations form a discrete-time point process. We derive the optimal (minimum mean squared error) filtered estimate which intrinsically fuses the image-based and primary observations. This optimal filter is computationally prohibitive but provides the basis for a clear understanding of various suboptimal approaches. We propose the image-enhanced interacting multiple model (IMM) filter as a practical alternative which retains many desirable properties of the optimal filter and outperforms existing image-enhanced tracking algorithms over a broad range of operating scenarios