An Action Recognition Method Based on Radar Signal with Improved GWO-SVM Algorithm

Abstract

As it is difficult to classify and identify the actions caused by the distortion of radar signal during acquisition process, this paper obtains the feature value of action signal through preprocessing such as abnormal point removal and wavelet filtering, and obtains the signal fluctuation section of action through short-term power spectral density. In the eight classification experiment and the nine classification experiment, the accuracies of traditional Bayesian network, BP network and support vector machine (SVM) are no higher than 90.0% For the test set with too small samples and some distortion, even using GWO-SVM, the recognition rate is still less than 90%. Therefore, this paper improves the wolf swarm position vector in GWO algorithm, and optimizes the penalty function and function radius in SVM model. The experimental results of our method show that the accuracies of eight classification and nine classification experiments are 92.4% and 90.4% respectively, which are better than those of SVM and GWO-SVM.