Multi-hypotheses based data association for scatter centers of spin cone-shape target

MHT approach is regarded as the best technique for multi-target tracking under low detection probability and dense clutter or false targets environments. Based on the K-best assignment algorithm, MHT approach assumes that a measurement is a false alarm, the start of a new track, and a potential update for multiple tracks in the database, and then generates the associated hypotheses according to the K-best assignment technique. When the conflict happens among data association, the determination is delayed to make until more measurements are received. This paper applies the K-best based MHT approach to do data association for high-resolution extracting scattering centers, which will provide information for further target imaging or motion feature extraction. The effectiveness of the algorithm is confirmed by the computer simulation. The result shows that MHT has good performance under dense clutter and false alarm environments.