BAYESIAN MULTI-TARGET TRACKING WITH SUPERPOSITIONAL MEASUREMENTS USING LABELED RANDOM FINITE SETS

被引：0

作者：

Papi, Francesco ^{[1
]}

Kim, Du Yong ^{[1
]}

机构：

[1] Curtin Univ, Dept Elect & Comp Engn, Bentley, WA 6102, Australia

来源：

2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2015年

关键词：

HYPOTHESIS DENSITY FILTER; KNOWLEDGE EXPLOITATION; IMPLEMENTATIONS;

D O I：

暂无

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：

In this paper we present a general solution for multi-target tracking problems with superpositional measurements. In a superpositional sensor model, the measurement collected by the sensor at each time step is a superposition of measurements generated by each of the targets present in the surveillance area. We use the Bayes multi-target filter with Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories. We propose an implementation of this filter using Sequential Monte Carlo (SMC) methods with an efficient multi-target sampling strategy based on the Approximate Superpositional Cardinalized Probability Hypothesis Density (CPHD) filter.

引用

页码：2211 / 2215

页数：5

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