Feature Selection for Classification of Human Micro-Doppler

Abstract

Dozens of features have been proposed for the use in a variety of human micro-Doppler classification problems, such as activity classification, target identification, and arm swing detection. However, the issues of how many features are truly required, which features should be selected, and whether or how this selection will vary depending upon human activity has not yet been rigorously addressed in the context of human micro-Doppler analysis. Moreover, most classification results are present for the case when the human directly walks towards or away from the radar. As the aspect angle between target and antenna increases, the observed micro-Doppler spread diminishes, leading to increasingly poor feature estimates. Thus, there is also a question of how features should be selected by taking into consideration estimate quality. This work examines the application of information theory to shed light on these questions. Mutual information is used to compute the contribution of features as a function of physical relevance and estimate quality. An importance ranking of features is derived, with results shown for arm swing detection and discrimination of walking from running.