Radar Working Mode Recognition Based on Multi-Scale Attention Mechanism ResNet
Mode recognition is a basic task to interpret radar behavior and function.Under the condition of flexi-ble signal and complex environment,the existing methods are difficult to screen out the redundant information in differ-ent spaces and channels in the pulse sequence.In this paper,based on the deep residual network,a spatial self-attention module and a channel self-attention module are added to adapt to the above signal characteristics.The self-attention mechanism is introduced in the model to realize the adaptive weight allocation of different spaces and channels of radar sequence,so that the network can focus on more diverse information more efficiently.The high precision recognition of radar working mode is realized under extreme conditions.Compared with classical deep learning networks such as AlexNet,LeNet,VGGNet,ResNet and conventional deep convolutional networks,the average recognition rate of this model is improved by 36%under the condition of 0～50%leakage pulses.In the independent test set,the model still has a recognition rate of more than 90%under the 40%leakage pulse.The advantages and effectiveness of the proposed net-work are proved.
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