A flexible data fusion architecture for persistent surveillance using ultra-low-power wireless sensor networks
2011
We have developed a flexible, target-driven, multi-modal, physics-based fusion architecture that efficiently searches
sensor detections for targets and rejects clutter while controlling the combinatoric problems that commonly arise in datadriven
fusion systems. The informational constraints imposed by long lifetime requirements make systems vulnerable to
false alarms. We demonstrate that our data fusion system significantly reduces false alarms while maintaining high
sensitivity to threats. In addition, mission goals can vary substantially in terms of targets-of-interest, required
characterization, acceptable latency, and false alarm rates. Our fusion architecture provides the flexibility to match these
trade-offs with mission requirements unlike many conventional systems that require significant modifications for each
new mission.
We illustrate our data fusion performance with case studies that span many of the potential mission scenarios including
border surveillance, base security, and infrastructure protection. In these studies, we deployed multi-modal sensor nodes
- including geophones, magnetometers, accelerometers and PIR sensors - with low-power processing algorithms and
low-bandwidth wireless mesh networking to create networks capable of multi-year operation. The results show our data
fusion architecture maintains high sensitivities while suppressing most false alarms for a variety of environments and
targets.
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