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Proceedings Paper

Probabalistic strongest neighbor filter for tracking in clutter
Author(s): X. Rong Li; Xiaorong Zhi
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Paper 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.

Paper Details

Date Published: 31 May 1996
PDF: 12 pages
Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); doi: 10.1117/12.241211
Show Author Affiliations
X. Rong Li, Univ. of New Orleans (United States)
Xiaorong Zhi, Univ. of New Orleans (United States)

Published in SPIE Proceedings Vol. 2759:
Signal and Data Processing of Small Targets 1996
Oliver E. Drummond, Editor(s)

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