Optimal polynomial filtering for planar tracking via virtual measurement process

In this paper it is studied the classical problem of target tracking by a new approach consisting in the treatment of the classical nonlinear measurement process in a form amenable for polynomial filtering without the need of the measure map linearization, as required by other standard sub-optimal algorithms. The main idea is to transfer the nonlinearity of the measure map into a modification of the noise sequence distribution in a nongaussian white sequence. This is indeed the property required for Kalman filtering which, although non more optimal, remains to be the optimal linear filtering algorithm. Conditions for polynomial filtering are also satisfied, allowing to face the nongaussian nature of the modified noise sequence. Simulations show high performances of the proposed algorithm.