SWATT: Synchronized Wide-area Sensing and Autonomous Target Tracking
2018 IEEE International Symposium on Technologies for Homeland Security (HST)(2018)
Abstract
Monitoring large land areas is a human-intensive process. Sensor networks offer the promise of automation. Modern sensor networks alone typically only address the problem of event detection; they do not not address having to analyze what is detected. Furthermore, despite advances in low-power, long-range embedded sensing as well as high-power, high-fidelity camera or UAS sensing, no one type of sensing system alone can be low-cost, widely deployed, and high-fidelity.We propose a hybrid network of low-power and high-power sensors that, together, offer wide-area coverage. We couple these with a mechanism for online machine learning, allowing the the network to react to events autonomously. We design, deploy and evaluate a proof-of-concept system that is able to detect, learn about and track a vehicle in real time based on this combination of multi-modal sensing and machine learning.
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Key words
unmanned aerial system,UAS sensing system,low-power sensors,multimodal sensing,synchronized wide-area sensing,sensor networks,autonomous target tracking,SWATT,proof-of-concept system,online machine learning,high-power sensors,hybrid network,high-fidelity camera,long-range embedded sensing,event detection,human-intensive process
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