Particle filter for tracking linear Gaussian target with nonlinear observations

被引：0

作者：

Ooi, A ^{[1
]}

Doucet, A ^{[1
]}

Vo, BN ^{[1
]}

Ristic, B ^{[1
]}

机构：

[1] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia

来源：

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

关键词：

target tracking; particle filter; linear Gaussian target; nonlinear observations;

D O I：

10.1117/12.487496

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a solution to the TENET nonlinear filtering challenge is presented.* The proposed approach is based on particle filtering techniques. Particle methods have already been used in this context but our method improves over previous work in several ways: better importance sampling distribution, variance reduction through Rao-Blackwellisation etc. We demonstrate the efficiency of our algorithm through simulation.

引用

页码：59 / 70

页数：12

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