Modified spatial clutter sparsity estimation for closely-spaced target tracking

The number of targets and the origin of measurement point are uncertain in target tracking, and measurement origins are always prior unknown. An accurate estimation result of clutter measurement density can provide a significant benefit in target tracking. However, the measurements caused by target give a bias of the clutter measurement density estimation, especially in the situation of closely-spaced target tracking. In this paper we proposed a new unbiased method to eliminate the effect of measurements from target and improve the performance of target tracking, the new method is a modification on spatial clutter measurement sparsity estimator. In the new method we subtract the probability of target existence from the number of measurement we used for clutter density estimation in order to get the unbiased estimation of clutter intensity. The simulation study results validate the effectiveness of the proposed new method.