A data-driven approach for processing heterogeneous categorical sensor signals

Christopher P. Calderon; Austin Jones; Scott Lundberg; Randy Paffenroth

doi:10.1117/12.894671

16 September 2011 A data-driven approach for processing heterogeneous categorical sensor signals

Christopher P. Calderon, Austin Jones, Scott Lundberg, Randy Paffenroth

Proceedings Volume 8137, Signal and Data Processing of Small Targets 2011; 813704 (2011) https://doi.org/10.1117/12.894671
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

False alarms generated by sensors pose a substantial problem to a variety of fusion applications. We focus on situations where the frequency of a genuine alarm is "rare" but the false alarm rate is high. The goal is to mitigate the false alarms while retaining power to detect true events. We propose to utilize data streams contaminated by false alarms (generated in the field) to compute statistics on a single sensor's misclassification rate. The nominal misclassification rate of a deployed sensor is often suspect because it is unlikely that these rates were tuned to the specific environmental conditions in which the sensor was deployed. Recent categorical measurement error methods will be applied to the collection of data streams to "train" the sensors and provide point estimates along with confidence intervals for the parameters characterizing sensor performance. By pooling a relatively small collection of random variables arising from a single sensor and using data-driven misclassification rate estimates along with estimated confidence bands, we show how one can transform the stream of categorical random variables into a test statistic with a limiting standard normal distribution. The procedure shows promise for normalizing sequences of misclassified random variables coming from different sensors (with a priori unknown population parameters) to comparable test statistics; this facilitates fusion through various downstream processing mechanisms. We have explored some possible downstream processing mechanisms that rely on false discovery rate (FDR) methods. The FDR methods exploit the test statistics we have computed in a chemical sensor fusion context where reducing false alarms and maintaining substantial power is important. FDR methods also provide a framework to fuse signals coming from non-chem/bio sensors in order to improve performance. Simulation results illustrating these ideas are presented. Extensions, future work and open problems are also briefly discussed.

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Christopher P. Calderon, Austin Jones, Scott Lundberg, and Randy Paffenroth "A data-driven approach for processing heterogeneous categorical sensor signals", Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 813704 (16 September 2011); https://doi.org/10.1117/12.894671

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KEYWORDS

Sensors

Statistical analysis

Environmental sensing

Error analysis

Signal detection

Monte Carlo methods

Binary data

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