Human micro-Doppler frequency estimation by CK-Hough joint algorithm

To accurately extract the micro-Doppler(m-D) frequency of a specific part of a moving target from the radar echo signal, a novel CK-Hough algorithm was proposed, which effectively combines the cluster analysis and the K Nearest Neighbor-Hough(KNN-Hough) algorithm. Firstly, the time-frequency spectrogram of the echo signal was obtained by a short-time Fourier transform(STFT). Secondly, the time-frequency spectrogram was clustered by the adaptive fuzzy C-means(AFCM) algorithm to obtain the frequency ranges of the different scattering parts of the human body and effectively inhibit the mutual interference between the components. The data pre-processing technique was used to adaptively adjust the number of clustering categories c, which adapts to diversified application scenarios. Thirdly, the improved KNN algorithm was utilized to enhance the correlation between the clustering results of adjacent moments and to fit the instantaneous frequency curve. Finally, the Hough transform was employed to determine the weight value of μ. The results show that the CK-Hough proposed in the paper extracts the m-D frequencies of human limbs in straight/curved walking scenarios. Compared with the traditional peak search algorithm, linear prediction Viterbi algorithm and frequency fitting algorithm based on the Bezier-Hough model, the CK-Hough algorithm proposed reduces the estimation error rate of frequency by 40.40%, 45.47% and 26.16%, respectively. Furthermore, in the curve walking experimental scenario, its estimation error rate decreases by 58.35%, 68.35% and 41.65%, respectively. © 2024 Central South University of Technology. All rights reserved.