Abstract
A key challenge for radar surveillance 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 few decades dozens of micro-Doppler features have been proposed, when in fact utilisation of all possible features does not guarantee the 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. Algorithms for optimising classification performance for a reduced number of features are presented. Performance gains achievable using adaptive feature selection are assessed for a case study of interest.
Published Version
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