Distributed signal decorrelation in wireless sensor networks using the sparse matrix transform

Leonardo R. Bachega; Srikanth Hariharan; Charles A. Bouman; Ness Shroff

doi:10.1117/12.887549

3 June 2011 Distributed signal decorrelation in wireless sensor networks using the sparse matrix transform

Leonardo R. Bachega, Srikanth Hariharan, Charles A. Bouman, Ness Shroff

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 80580V (2011) https://doi.org/10.1117/12.887549
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

In this paper, we propose the vector SMT, a new decorrelating transform suitable for performing distributed anomaly detection in wireless sensor networks (WSN). Here, we assume that each sensor in the network performs vector measurements, instead of a scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor measurements, until the vectors from all sensors are completely decorrelated. We perform simulations with a network of cameras, where each camera records an image of the monitored environment from its particular viewpoint. Results show that the proposed transform effectively decorrelates image measurements from the multiple cameras in the network. Because it enables joint processing of the multiple images, our method provides significant improvements to anomaly detection accuracy when compared to the baseline case when we process the images independently.

Citation Download Citation

Leonardo R. Bachega, Srikanth Hariharan, Charles A. Bouman, and Ness Shroff "Distributed signal decorrelation in wireless sensor networks using the sparse matrix transform", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580V (3 June 2011); https://doi.org/10.1117/12.887549

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