Classification of Unintended Radiated Emissions in a Multi-Device Environment

Abstract

Unintended radiated emissions (URE) from electronic devices are conducted on to the power infrastructure and can be collected and analyzed for non-intrusive load monitoring (NILM) applications. Dimensionally aligned signal projections (DASP) were previously introduced as specialized signal transforms to enable device classification using machine learning on statistical features derived from the DASP transforms. In this work, we explore multi-device classification which requires more sophisticated methods owing to the complexity of the feature space such as large dynamic range, non-contiguous features, over-lapping features, and nonlinear interactions of features. In particular, we introduce an additional DASP algorithm for increased resolution and sensitivity to intermodulation products and examine the utility of convolutional neural networks (CNNs) to extract and learn features directly from DASP images for classification of single and multiple device URE captures.