Multiple hypotheses tracking with heavy-tailed noise

被引：1

作者：

Sims, SW ^{[1
]}

Ralph, JF ^{[1
]}

Smith, MI ^{[1
]}

Angell, CR ^{[1
]}

Randall, P ^{[1
]}

机构：

[1] Univ Liverpool, Dept Elect & Elect Engn, Liverpool L69 3GJ, Merseyside, England

来源：

SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XII | 2003年 / 5096卷

关键词：

targeting; object localisation; data fusion; multiple hypothesis tracking;

D O I：

10.1117/12.487007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Kalman filter, which is optimal with respect to Gaussian distributed noisy measurements, is commonly used in the Multiple Hypothesis Tracker (MHT) for state update and prediction. It has been shown that when filtering noisy measurements distributed with asymptotic power law tails the Kalman filter underestimates the state error when the tail exponent is less than two and overestimates it when the tail exponent is greater that two. This has severe implications for tracking with the MHT which uses the estimated state error for both gating and probability calculations. This paper investigates the effects of different tail exponent values on the processes of hypothesis creation and deletion in the MHT.

引用

页码：71 / 79

页数：9