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16 April 2008 Analysis of scan and batch processing approaches to static fusion in sensor networks
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Multi-sensor tracking holds the potential for improving the surveillance performance achieved through single-sensor tracking. This potential has been demonstrated in many domains: at NURC, in the context of multi-static undersea surveillance. Nonetheless, the issue remains of how best to process data in large sensor networks. This issue is taken up in this paper. We are interested to compare multi-sensor scan-based tracking with a two-stage approach: static fusion followed by scan-based tracking. This paper focuses on some candidate methodologies for static fusion. The methods developed in this paper fall into two categories. The scan-based approach leverages the Gaussian mixture probabilistic hypothesis density (GM-PHD) filter; the batch approaches are based on scan statistics, and on the multi-hypothesis PDA (MHPDA). Preliminary simulation-based performance analysis suggests that the MHPDA approach to static fusion is the most robust in dealing with closely spaced targets and small sensor networks. Leveraging the results presented here, follow-on work will address the determination of an optimal fusion and tracking architecture. In particular, we will test scan-based tracking based on the NURC distributed multi-hypothesis tracker (DMHT), with MHPDA processing followed by scan-based tracking (with the DMHT). We anticipate that, for large sensor networks, the latter approach will outperform the former.
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Marco Guerriero, Stefano Coraluppi, and Peter Willett "Analysis of scan and batch processing approaches to static fusion in sensor networks", Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690Z (16 April 2008);

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