One-dimensional range profile recognition method of sea-surface targets based on DnCNN

Aiming at the problem of poor target classification and recognition accuracy under low signal-to-noise ratio(SNR) conditions, a high resolution one-dimensional range profile(HRRP) recognition method for sea-surface targets based on denoising convolutional neural networks(DnCNN) is proposed. The proposed method designed a sea surface target classification and recognition model, which improves the signal-to-noise ratio through the noise reduction module. First, the similar characteristics of HRRP and two-dimensional images are analyzed, and HRRP noise reduction is transformed into two-dimensional image noise reduction. Secondly, the deep-level convolutional layer and the batch normalization layer are combined to extract the deep-level noise features of the image, and finally the residual learning technology is used to reduce the learning burden of the deep network while reconstructing the image for classification and recognition. Experimental results show that the model can greatly improve the accuracy of sea-surface target classification and recognition under the condition of low signal-to-noise ratio. What’s more, with the features of good recognition performance and robustness, its recognition performance is also better than that of contrast model under different conditions of SNR.