A Detection Method for Radar Weak Targets Based on ResNet
In order to solve the problem that Constant False Alarm Rate(CFAR)detection algorithm is difficult to detect radar weak targets,the target detection method based on Convolutional Neural Network(CNN)is studied.Taking full advantage of the excellent performance of neural networks in feature extraction,a radar weak target detection method based on the Residual Network(ResNet)block is proposed.This method breaks through the framework of traditional methods using only amplitude information for the object detection,and the phase features in radar echo data are fully mined as the basis for neural network object classification detection.According to experiments,the proposed method can still achieve a detection probability of over 50％even when the signal-to-noise ratio of the target echo is only-7 dB.Moreover,as the signal-to-noise ratio decreases,the superiority of the proposed method becomes more apparent.
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