Adaptive micro doppler data processing for environmental and operational conditions

A key challenge for radar survelliance systems is the discrimination of ground based targets, especially humans from animals, as well as different types of human activities. For this purpose, target micro-Doppler signatures have been shown to yield high automatic target classification rates; however, performance is typically only given for near-optimal operating conditions using a fixed set of features. Over the past decade a vast number of micro-Doppler features have been proposed for classification of radar micro-Doppler signatures. In fact utilization of all possible features does not guarantee maximum classification performance and the selection of an optimal subset of features is scenario dependent. In this work, a comprehensive survey of micro-Doppler features and their dependence upon system parameters and operational conditions – such as transmit frequency, range and Doppler resolution, antenna target geometry, signal to noise ratio, and dwell time – is given. A new feature design for human micro-Doppler signatures is proposed. Algorithms for optimizing classification performance for a reduced number of features are presented and it is shown that a well selected subset of robust features yields better results. Performance gains achievable using adaptive feature selection are assessed for a case study of interest.