convolutional neural network classification using three cepstrums combinations with time, time derivative and reassigned stft of doppler signatures from radar human locomotion

This paper introduces the ctdrcepstrum, a novel feature extraction technique designed to differentiate various human activities using doppler radar classification. real data were collected from a doppler radar system, capturing nine return echoes while monitoring three distinct human activities: walking, fast walking, and running. these activities were performed by three subjects, either individually or in pairs. we focus on analyzing the doppler signatures using time-frequency reassignment, emphasizing its advantages such as improved component separability. the proposed ctdrcepstrum explores different window functions, transforming each echo signal into three forms of short-time fourier transform reassignments (rstft): time rstft (tstft), time derivative rstft (tdstft), and reassigned stft (rstft). a convolutional neural network (cnn) model was then trained using the feature vector, which is generated by combining the cepstral analysis results of each rstft form. experimental results demonstrate the effectiveness of the proposed method, achieving a remarkable classification accuracy of 99.83% by using the bartlett-hanning window to extract key features from real-time doppler radar data of moving targets.