Probabalistic strongest neighbor filter for tracking in clutter

X. Rong Li; Xiaorong Zhi

doi:10.1117/12.241211

31 May 1996 Probabalistic strongest neighbor filter for tracking in clutter

X. Rong Li, Xiaorong Zhi

Proceedings Volume 2759, Signal and Data Processing of Small Targets 1996; (1996) https://doi.org/10.1117/12.241211
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

A simple and commonly used method for tracking in clutter to deal with measurement origin uncertainty 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 on-line calculated error standard deviations. Based on the theoretical results obtained recently of the SNF for tracking in clutter, a probabilistic strongest neighbor filter is presented here. 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 is not target-oriented, which is accomplished by using probabilistic weights.

Citation Download Citation

X. Rong Li and Xiaorong Zhi "Probabalistic strongest neighbor filter for tracking in clutter", Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); https://doi.org/10.1117/12.241211

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