Distributed vector decorrelation and anomaly detection using the vector Sparse Matrix Transform

Here, we propose the vector Sparse Matrix Transform (SMT), a novel decorrelating transform suitable for performing distributed processing of high dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a camera network monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Results show that the vector SMT effectively decorrelates images from the multiple cameras in the network and significantly improves anomaly detection accuracy while requiring low overall communication energy.