PSNF: a refined strongest neighbor filter for tracking in clutter

A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF). It uses the measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement location, known as the “strongest neighbor” measurement, as if it were the true one. Its performance is significantly better than that of the nearest neighbor filter (NNF) but usually worse than that of the probabilistic data association filter (PDAF), while its computational complexity is the lowest one among the three filters. The SNF is, however, not consistent in the sense that its actual tracking errors are well above its online calculated error standard deviations. Based on the theoretical results obtained with the SNF, a probabilistic strongest neighbor filter (PSNF) is presented. This new filter is consistent and is substantially superior to the PDAF in both performance and computation. The proposed filter is obtained by modifying the standard SNF to account for the probability that the strongest neighbor measurement is not target-originated, which is accomplished by using probabilistic weights