Dictionary Learning With Low Computational Complexity for Classification of Human Micro-Dopplers Across Multiple Carrier Frequencies

Recently, several machine learning algorithms have been applied for classifying micro-Doppler signatures from different human motions. However, these algorithms must demonstrate versatility in handling diversity in test and training data to be used for real-life scenarios. For example, situations may arise where the propagation channel or the presence of interference sources in the test site will permit only specific frequency bands of radar operation. These bands may differ from those used previously while training. In this paper, we examine the performances of three sparsity driven dictionary learning algorithms—synthesis, deep, and analysis—for learning unique features extracted from training data gathered across multiple carrier frequencies. These features are subsequently used for classifying test data from another distinct carrier frequency. Our experimental results, from measurement data, show that the dictionary learning algorithms are capable of extracting meaningful representations of the micro-Dopplers despite the rich frequency diversity in the data. In particular, the deep dictionary learning algorithm yields a high classification accuracy of 91% with a very low computational time for testing.