Research on UWB radar human motion recognition based on improved particle swarm optimization algorithm
For the clutter interference in radar signals and the limitation of the number of samples on the accuracy of human motion recognition,this paper proposes an ultra-wideband(UWB)radar human motion recognition algorithm based on im-proved particle swarm optimization(PSO)to optimize the support vector machine(SVM)model.Moving target indication(MTI)and wavelet threshold filtering are used to preprocess the received UWB echo signals to eliminate the influence of clutter and noise in the echo signals on human motion recognition.Two-dimensional discrete wavelet packet decomposition(2D-DWPD)and singular value decomposition(SVD)are combined to extract features and reduce dimensions of the pre-processed radar signals.An improved particle swarm optimization algorithm is proposed to optimize relevant parameters of the SVM model for recognition and classification.Experimental results show that the accuracy of the proposed algorithm can reach 96.25%,and it has good recognition performance.
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