Signature-aided tracking using association hypotheses
Craig S. Agate, Kevin Sullivan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
An algorithm is derived for signature-aided tracking which uses features (e.g. high-range resolution radar (HRRR) profiles), or functions of features, in addition to kinematic measurements to associate measurements to known tracks, clutter or new tracks. The approach taken here is to derive the probability of the measurement-to-track association hypotheses which incorporates the likelihood of features as well as the traditional approach of using the kinematic measurement likelihood. It is assumed that the probability density function (PDF) of the features (or some function of the features) is available from a library. The approach to probabilistically characterizing the PDF of the profiles relies on the availability of a class-specific library for each target type. The class-specific library of PDFs characterizes the profiles conditioned on the target class from which the profile originated and the aspect at which the profile was obtained. The algorithm is evaluated using the SLAMEM simulation.