In order to solve the problems of inconvenient application of traditional pattern recognition methods and low recognition rate when classifying human and vehicle ground motion signals with low signal-to-noise ratio in the field environment, the processing method and feature extraction method for this kind of signals are studied through the feature extraction algorithm based on envelope detection, variational mode decomposition (VMD) and improved depth self encoder (DAE). Firstly, the target ground motion signal is transformed by Hilbert transform to obtain the smooth envelope of the signal, then the envelope is decomposed by variational mode decomposition, the decomposed IMF signal is screened by correlation coefficient, and the components with high correlation are weighted and synthesized into an intermediate signal with high signal-to-noise ratio, and then its features are extracted from the improved depth self encoder. Finally, the random forest algorithm with good generalization performance is used to classify the signals, so as to realize the recognition and classification of human and vehicle targets. The results show that the accuracy of this algorithm is higher than other traditional algorithms. It can be seen that the algorithm has application value for this kind of target.
刘文杰,邹瑛珂,张珊,等. 基于VMD与改进DAE人车地震动包络信号识别算法[J]. 科学技术与工程, , ():复制