A multi-space data association algorithm for target tracking systems

In this paper, a multi-space data association algorithm based on the wavelet transform is proposed. In addition to carrying out the traditional hard logic data association in measurement space, the new algorithm updates the state of the target in the pattern space. Such a function significantly reduces the complicated environment misassociation effects on the data association. Simulation results show that the performance of the multi-spaced data association is much better than the existing data association algorithms in complicated clutter environments, such as the nearest-neighbor standard filter (NNSF), the probabilistic data association (PDA) and the joint probabilistic data association (JPDA). The computation of the multiple-space data association is much less than the aforementioned other existing data associations, and this new data association does not need any priori information of the environment. In complicated clutter environments, compared with the other data association, the new data association proposed in this paper is very robust, reliable and stable.