Deep learning model for micro-motion classification of cone targets

To overcome the shortcomings of traditional micro-motion classification of spatial cone targets, such as manual construction, feature extraction, and lack of generality, intelligence and poor classification performance under strong noise, a new network model combining a convolutional neural network and a bidirectional long short-term memory network is proposed. The network combines the residual network, inception network and bidirectional long short-term memory network into an integrated network. By increasing the depth and width of the network to mine the abstract features of higher dimensions, the classification accuracy of the network can be improved. The reasoning ability of the bidirectional long short-term memory network can improve the fault tolerance of the network, and the advantages of time series classification and the jumping bypass branch structure of the residual network can also reduce parameter redundancy and speed up network training. Simulation results show that the network model not only achieves faster intelligent classification, but also improves the accuracy of ResNet-18 and GoogLeNet models by 5% and 4% respectively, thus verifying the validity and generalization ability of the model.