Data-Dependent Micro-Doppler Feature Selection

Abstract

A vast number of features have been proposed over the years for classification of radar micro-Doppler signatures. However, the degree to which a feature may contribute in discriminating between classes depends upon a variety of operational considerations, such as antenna-target aspect angle, signal-to-noise ratio (SNR), and dwell time. Moreover, utilization of all features in every circumstance does not necessarily ensure optimal classification performance. Oftentimes a well-selected subset of robust features yield better results. In this work, the variance of micro-Doppler feature estimates are examined under a variety of operational conditions and used to select feature subsets. The classification performance of data-dependent feature subsets are compared to that attained without any feature selection. Results show that data-dependent feature selection yields higher correct classification rates over a wider range of operational situations.