Comparison of sampling-based algorithms for multisensor distributed target tracking

被引：0

作者：

Nguyen, TM ^{[1
]}

Jilkov, VP ^{[1
]}

Li, XR ^{[1
]}

机构：

[1] Univ New Orleans, Dept Elect Engn, New Orleans, LA 70148 USA

来源：

FUSION 2003: PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE OF INFORMATION FUSION, VOLS 1 AND 2 | 2003年

关键词：

target tracking; multisensor data fusion; nonlinear filtering; particle filter; unscented Kalman filter; unscented particle filter;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper considers the problem of tracking a maneuvering target in multisensor environment. A novel scheme is proposed for distributed tracking which utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is nonlinear In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented-particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results front extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving a distributed target tracking problem.

引用

页码：114 / 121

页数：8

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