Hangzhou Dianzi University

Xiaona Xue

Zhiqun Cheng

Jingyu Zhao

Jiangong Ni

Yejiang Lin

Zhigang Zhou

Dou Pei

An unmanned aerial vehicle (UAV) identification method based on blind source separation and deep learning is disclosed, in the method, firstly, the one-dimensional radar cross section millimeter wave data set of the UAV is acquired, and the mixed signal is obtained by mixing, and the improved FastICA algorithm is used to separate it. Secondly, the separated signal is converted into a two-dimensional image by data transformation, and the two-dimensional image is augmented, the obtained data set is divided into training set, validation set and test set. Thirdly, establish a UAV classification model based on Improved ResNet18 and train this model on the training set to achieve UAV classification. In the present invention, the training time of the network is not greatly increased while the network identification accuracy is improved, so that it can well complete the UAV type identification, and the design is more reasonable and effective.

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Hangzhou, China

Xiaona Xue