Distributed Fusion of PHD Filters Via Exponential Mixture Densities

被引:171
作者
Ueney, Murat [1 ,2 ]
Clark, Daniel E. [1 ,2 ]
Julier, Simon J. [3 ]
机构
[1] Heriot Watt Univ, Sch Engn & Phys Sci, Edinburgh EH14 1AS, Midlothian, Scotland
[2] Edinburgh Res Partnership, Joint Res Initiat Signal & Image Proc, Edinburgh, Midlothian, Scotland
[3] UCL, Dept Comp Sci, London WC1E 6BT, England
基金
英国工程与自然科学研究理事会;
关键词
Multi-object filtering; PHD; CPHD; multi-sensor fusion; distributed fusion; exponential mixture density; covariance intersection; multi-sensor multi-target tracking; wireless sensor networks;
D O I
10.1109/JSTSP.2013.2257162
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 consider the problem of Distributed Multi-sensor Multi-target Tracking (DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple hypothesis tracking and track-to-track fusion. However, there are two difficulties with these approaches. First, the computational costs of these algorithms can scale factorially with the number of hypotheses. Second, consistent optimal fusion, which does not double count information, can only be guaranteed for highly constrained network architectures which largely undermine the benefits of distributed fusion. In this paper, we develop a consistent approach for DMMT by combining a generalized version of Covariance Intersection, based on Exponential Mixture Densities (EMDs), with Random Finite Sets (RFS). We first derive explicit formulae for the use of EMDs with RFSs. From this, we develop expressions for the probability hypothesis density filters. This approach supports DMMT in arbitrary network topologies through local communications and computations. We implement this approach using Sequential Monte Carlo techniques and demonstrate its performance in simulations.
引用
收藏
页码:521 / 531
页数:11
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