PARTICLE FILTERING IN THE PRESENCE OF OUTLIERS

被引:8
|
作者
Maiz, Cristina S. [1 ]
Miguez, Joaquin [1 ]
Djuric, Petar M. [2 ]
机构
[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, E-28903 Getafe, Spain
[2] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY USA
基金
美国国家科学基金会;
关键词
Particle filtering; outlier detection; spatial depth; nonlinear tracking;
D O I
10.1109/SSP.2009.5278645
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a recently proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.
引用
收藏
页码:33 / +
页数:2
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