Micro-Motion Classification of Cone Targets Based on AlexNet-BiLSTM Network

Aiming at the problem that the classification of typical ballistic cone targets needs to construct and extract artificial features, but lacks generality and intelligence, a new method of intelligent classification of ballistic cone targets based on micro-motion time-frequency diagram is proposed, which combines Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). Firstly, the micro-Doppler characteristics of the ballistic cone target are analyzed, and the micro-Doppler frequencies of different micro-motion forms are obtained; then, the AlexNet-BiLSTM network model is constructed by using the image feature extraction ability of AlexNet network and the temporal feature extraction ability of BiLSTM network, and the network is trained and debugged by using time-frequency diagram data set generated by simulated radar echoes. Finally, the simulation results show that the network can achieve intelligent micro-motion classification of ballistic cone targets, which verifies the effectiveness and robustness of the network.