Evidence filtering

被引：31

作者：

Dewasurendra, Duminda A. ^{[1
]}

Bauer, Peter H.

Premaratne, Kamal

机构：

[1] Univ Notre Dame, Dept Elect Engn, Mobile Sensing Syst MOSES Lab, Notre Dame, IN 46556 USA

[2] Univ Miami, Dept Elect & Comp Engn, DEE Lab, Coral Gables, FL 33124 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2007年 / 55卷 / 12期

基金：

美国国家科学基金会;

关键词：

dempster-Shafer (DS) belief theory; evidence filters; filter design; partial and incomplete information; positive systems; sensor modalities; signature detection; spatiotemporal sensor data;

D O I：

10.1109/TSP.2007.900759

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：

A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the "frequency" characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering.

引用

页码：5796 / 5805

页数：10

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