Mertens, MichaelUlmke, MartinFähnrich, Klaus-PeterFranczyk, Bogdan2019-01-112019-01-112010978-3-88579-270-3https://dl.gi.de/handle/20.500.12116/19319Many surveillance tasks rely on the generation of stable, continuous tracks of objects of interest. Often, track continuity has a higher priority than track accuracy, as valuable information on the identity or the origin of an object is lost by each track drop. The main source of track fragmentation are missing detections due to a limited field of view or technical or topographical masking. In the multi-target case, in addition, individual tracks can be interchanged by unknown data assignments. Therefore, the exploitation of additional sensor data is required in order to discriminate individual objects and to associate track fragments. As signal strength measurements are standard output of modern radar systems, the integration of this information into a Bayesian tracking scheme is discussed in the present paper. 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 estimated sequentially. 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 fluctuations 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 two-target simulation scenario.enText/Conference Paper1617-5468