Radar micro-Doppler feature extraction using the Singular Value Decomposition

Abstract

The micro-Doppler spectrogram depends on parts of a target moving and rotating in addition to the main body motion (e.g., spinning rotor blades) and is thus characteristic for the type of target. In this study, the micro-Doppler spectrogram is exploited to distinguish between birds and small unmanned aerial vehicles (UAVs). The focus hereby is on micro-Doppler features enabling fast classification of birds and mini-UAVs. In a second classification step, it is desired to exploit micro-Doppler features to further characterize the type of UAV, e.g., fixed-wing vs. rotary-wing. In this paper, potentially robust features are discussed supporting the first classification step, i.e., separation of birds and UAVs. The Singular Value Decomposition seems a powerful tool to extract such features, since the information content of the micro-Doppler spectrogram is preserved in the singular vectors. In the paper, some examples of micro-Doppler feature extraction via Singular Value Decomposition are given.