TY - CHAP
T1 - PSNF
T2 - Proceedings of the 1996 35th IEEE Conference on Decision and Control. Part 3 (of 4)
AU - Li, X. Rong
AU - Zhi, Xiaorong
PY - 1996
Y1 - 1996
N2 - 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 on-line calculated error standard deviations. Based on the theoretical results obtained recently of the SNF, a probabilistic strongest neighbor filter (PSNF) 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 measurement is not target-originated, which is accomplished by using probabilistic weights.
AB - 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 on-line calculated error standard deviations. Based on the theoretical results obtained recently of the SNF, a probabilistic strongest neighbor filter (PSNF) 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 measurement is not target-originated, which is accomplished by using probabilistic weights.
UR - https://www.scopus.com/pages/publications/0030406381
M3 - 章节
AN - SCOPUS:0030406381
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2355
EP - 3592
BT - Proceedings of the IEEE Conference on Decision and Control
A2 - Anon, null
Y2 - 11 December 1996 through 13 December 1996
ER -