SEQUENTIAL MARKOV CHAIN MONTE CARLO FOR MULTI-TARGET TRACKING WITH CORRELATED RSS MEASUREMENTS

被引：0

作者：

Lamberti, Roland ^{[1
]}

Septier, Francois ^{[2
]}

Salman, Naveed ^{[3
]}

Mihaylova, Lyudmila ^{[3
]}

机构：

[1] Inst Mines Telecom, Telecom SudParis, Paris, France

[2] Inst Mines Telecom, Telecom Lille, CRIStAL UMR CNRS 9189, Paris, France

[3] Univ Sheffield, Sheffield S10 2TN, S Yorkshire, England

来源：

2015 IEEE TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (ISSNIP) | 2015年

关键词：

Tracking; Correlated shadowing; Bayesian inference; Sequential MCMC; TARGET;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a Bayesian approach to accurately track multiple objects based on Received Signal Strength (RSS) measurements. This work shows that taking into account the spatial correlations of the observations caused by the random shadowing effect can induce significant tracking performance improvements, especially in very noisy scenarios. Additionally, the superiority of the proposed Sequential Markov Chain Monte Carlo (SMCMC) method over the more common Sequential Importance Resampling (SIR) technique is empirically demonstrated through numerical simulations in which multiple targets have to be tracked.

引用

页数：6

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