The Marginalized Auxiliary Particle Filter

被引：0
|
作者：
Fritsche, Carsten [1 ]
Schon, Thomas B. [2 ]
Klein, Anja [1 ]
机构：
[1] Tech Univ Darmstadt, Inst Telecommun, Merckstr 25, D-64287 Darmstadt, Germany
[2] Linkoping Univ, Div Automat Control, SE-58183 Linkoping, Sweden
来源：
2009 3RD IEEE INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2009) | 2009年
基金：
瑞典研究理事会;
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we are concerned with nonlinear systems subject to a conditionally linear, Gaussian sub-structure. This structure is often exploited in high-dimensional state estimation problems using the marginalized (aka Rao-Blackwellized) particle filter. The main contribution in the present work is to show how an efficient filter can be derived by exploiting this structure within the auxiliary particle filter. Based on a multi-sensor aircraft tracking example, the superior performance of the proposed filter over conventional particle filtering approaches is demonstrated.
引用
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页码：289 / +
页数：3
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