Comparison of nearest neighbor and probabilistic data association methods for non-linear target tracking data association

Target tracking problems are theoretically interesting, because the origins of the measurements are not identified. Data association is one of the key techniques on tracking with radar. The problem of data association for target tracking in a cluttered environment with linear target model and non-linear measurement model will be discussed. Firstly, evidences are constructed based on spherical coordinates. Then, the association decisions are constructed according to nearest neighbor and probabilistic data association methods. The simulation results show that the latter method has better performance than the former. Moreover, the results will be compared to linear target tracking, which is really common in data association techniques and it will be shown that there will be a slight decrease in performance of target tracking with nonlinear measurement model.