Classification of micro-Doppler signatures with the use of orthogonal moment-based features

Abstract

Radar micro-Doppler signatures are powerful indicators of target movements and activities, enabling the extraction of valuable information about various objects' internal and external dynamics. Consequently, classifying these signatures has become crucial in numerous applications, ranging from target recognition in surveillance, to biomedical sensing and interaction with smart sensors.
In this thesis, an evaluation of classification performances for a wide variety of orthogonal moments, when applied to micro-Doppler classification problems, is presented. A pipeline is proposed to evaluate all moments commonly used in image processing, but not routinely employed in radar-based classification.
The evaluation results are compared with other state-of-the-art classification approaches, such as using micro-Doppler signatures directly as the input of Convolutional Neural Networks. The influence of noise in the data on the classification performance is also shown.
The classification results demonstrate the different moments' capabilities with a variety of publicly available datasets containing human micro-Doppler signatures, resulting in a very well performing classification pipeline for this type of classification problem, and novel insights into the potential of these moments for radar classification problems.