A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment

A new probabilistic nearest neighbor (NN) filter algorithm which accounts for the probability that the NN measurement is a false one is proposed to improve the performance of the NN filter. The NN filter is the most popular and widely used algorithm for target tracking in clutter due to its computational simplicity. The proposed algorithm is derived from establishing probability density functions conditioned on all the possible events related to the NN measurement. The resulting algorithm is different from the existing probabilistic nearest neighbor filter (PNNF) algorithm. The performance of the proposed algorithm is analyzed and compared with that of the NNF. The proposed algorithm for aerial target tracking in a clutter environment is tested by a series of Monte Carlo simulation runs. Simulation results are also compared with the off-line performance prediction algorithm developed in this paper.