Distributed fusion of multitarget densities and consensus PHD/CPHD filters

被引：41

作者：

Battistelli, G. ^{[1
]}

Chisci, L. ^{[1
]}

Fantacci, C. ^{[1
]}

Farina, A. ^{[2
]}

Mahler, R. P. S. ^{[3
]}

机构：

[1] Univ Florence, Dipartimento Ingn Informat, I-50139 Florence, Italy

[2] Selex ES, CTO, I-00131 Rome, Italy

[3] Lockheed Martin, San Antonio, TX USA

来源：

SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXIV | 2015年 / 9474卷

关键词：

Multitarget tracking; sensor networks; cardinalized PHD filter; consensus; RANDOM FINITE SETS;

D O I：

10.1117/12.2176948

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：

The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network.

引用

页数：15

共 32 条

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[2]

[Anonymous], 1995, Multitarget-Multisensor Tracking: Principles and Techniques

[3]

[Anonymous], 1988, Tracking and Data Association

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