On maneuvering target tracking via the PMHT

被引：0

作者：

Logothetis, A ^{[1
]}

Krishnamurthy, V ^{[1
]}

Holst, J ^{[1
]}

机构：

[1] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3052, Australia

来源：

PROCEEDINGS OF THE 36TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5 | 1997年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an iterative off-line optimal state estimation algorithm, which yields the maximum a posteriori (MAP) state trajectory estimate of the state sequence of a target maneuvering in clutter. The problem is formulated as a jump Markov linear system and the Expectation Maximization algorithm is used to compute the state sequence estimate. The proposed algorithm optimally combines a Hidden Markov Model and a Kalman Smoother to yield the MAP target state sequence estimate. The algorithm proposed, uses Probabilistic Multi-Hypothesis Tracking (PMHT) techniques for tracking a single maneuvering target in clutter. Previous applications of the PMHT technique [3] have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal (in a MAP sense) tracking of a maneuvering target in clutter.

引用

页码：5024 / 5029

页数：6