Multi-Bernoulli smoother for multi-target tracking

Dong Li

doi:10.1016/j.ast.2015.11.017

2016-01-01

PRE

Dong Li *

Chenping Hou

Dongyun Yi

DOI：10.1016/j.ast.2015.11.017

原文链接

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摘要

En 中文

Multi-target tracking is an important research topic in the field of aerospace. In this paper, a multi Bernoulli smoother, which consists of forward filtering followed by backward smoothing, is proposed for multi-target tracking. The forward filtering is accomplished by the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter. For the backward smoothing, the smoothed multi-target probability density is approximated by a multi-Bernoulli density, whose backward recursion is derived by using finite set statistics. To solve the computational problem of multiple integrals in the smoother, a sequential Monte Carlo method is also proposed. Experimental results show that the proposed smoother improves the estimation accuracy of target number and target states over the CBMeMBer filter. (C) 2015 Elsevier Masson SAS. All rights reserved.

Keyword:

Filtering

Finite set statistics

Multi-Bernoulli

Smoothing

Tracking

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期刊

Aerospace Science and Technology

IF:

5.8

论文数:

1.0W

被引数:

3.0W

机构

national university of defense technology - china

学者数:

1.8W

论文数: 1.4W

被引数: 9

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