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16 April 2008Analysis of scan and batch processing approaches to static fusion in sensor networks
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, 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); https://doi.org/10.1117/12.777633