Abstract
In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference.
Highlights
With the development and changes of our society, more and more territory emergency occurred, human detection and recognition have gradually become the main research techniques for security and surveillance systems
Radar technology has a lot of advantages such as it will not be influenced by light intensity so that radar can detect targets in low visibility weather and it can penetrate block clothes and so on
The application of human activity recognition and classification based on micro-Doppler motion feature has become an area of interest in recent years
Summary
With the development and changes of our society, more and more territory emergency occurred, human detection and recognition have gradually become the main research techniques for security and surveillance systems. There is great research value and application significance in human activity detection and feature extraction by using radar technology. In [8,9,10], the authors used a continuous wave radar to target human activities, which consist of walking, running, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still They introduced deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The existence of endpoint effect limits the practical application of HHT algorithm It still lacks the value research of the characteristics of human body signals after the completion of the HHT analysis. The existing relevant research focusing on the classification algorithm or feature extraction algorithm are relatively simple in time-frequency analysis of micro-Doppler motion signals. By comparing the experimental results in different environments, the higher accuracy of the system is verified, and the stronger anti-interference performance of the system is verified by interference experiments
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