A Continuous Probabilistic Origin Association Filter for Extended Object Tracking

One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.