ADAPTIVE BAYESIAN TRACKING WITH UNKNOWN TIME-VARYING SENSOR NETWORK PERFORMANCE

被引：0

作者：

Papa, Giuseppe ^{[1
]}

Braca, Paolo ^{[1
]}

Horn, Steven ^{[1
]}

Marano, Stefano ^{[2
]}

Matta, Vincenzo ^{[2
]}

Willett, Peter ^{[3
]}

机构：

[1] NATO STO CMRE, La Spezia, Italy

[2] Univ Salerno, Fisciano, Italy

[3] Univ Connecticut, Storrs, CT USA

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | 2015年

关键词：

Multiple sensors; real-world data; Bayesian target tracking; particle filter; time-varying performance; FILTERS; TUTORIAL; SNR; PHD;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The proposed tracking strategy is based on a Bayesian framework, in which the dynamic target state is augmented to include the sensor detection probabilities. The method is validated using computer simulations and real-world experiments conducted by the NATO Science and Technology Organization (STO) - Centre for Maritime Research and Experimentation (CMRE).

引用

页码：2534 / 2538

页数：5

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