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7 May 2012 Detecting clustered chem/bio signals in noisy sensor feeds using adaptive fusion
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Chemical and biological monitoring systems are faced with the challenge of detecting weak signals from contam- inants of interest while at the same time maintaining extremely low false alarm rates. We present methods to control the number of false alarms while maintaining power to detect; evaluating these methods on a fixed sensor grid. Contaminants are detected using signals produced from underlying sensor-specific detection algorithms. By learning from past data, an adaptive background model is constructed and used with a multi-hypothesis testing method to control the false alarm rate. Detection methods for chemical/biological releases often depend on specific models for release types and missed detection rates at the sensors. This can be problematic in field situations where environment specific effects can alter both a sensor's false alarm and missed detection characteristics. Using field data, the false alarm statistics of a given sensor can be learned and used for inference; however the missed detection statistics for a sensor are not observable while in the field. As a result, we pursue methods that do not rely on accurate estimates of a sensor's missed detection rate. This leads to the development of the Adaptive Regions Method that under certain assumptions is designed to conservatively control the expected rate of false alarms produced by a fusion system over time, while maintaining power to detect.
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Scott Lundberg, Chris Calderon, and Randy Paffenroth "Detecting clustered chem/bio signals in noisy sensor feeds using adaptive fusion", Proc. SPIE 8393, Signal and Data Processing of Small Targets 2012, 839303 (7 May 2012);

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