Abstract
Signal strength information is a standard output of a modern radar system. Provided the amplitude of the target returns exceeds the false alarm background, the consideration of signal strength may lead to improved target estimates, depending on the scenario. In this paper a Bayesian tracking algorithm is presented which incorporates the signal strength information. In contrast to previous approaches, the knowledge on the target's signal strength is not only used for an improved calculation of the association probabilities, but it enters into the algorithm as a random variable which is sequentially estimated. By this approach it is not only possible to discriminate closely-spaced targets and improve the track continuity, but also to support possible classification and identification tasks. The signal strength uctuations of the target returns are modeled by the Swerling-I and Swerling-III cases. As a first performance evaluation, numerical results are presented based on a multi-target simulation scenario.