Radar HRRP target recognition based on stacked denosing sparse autoencoder

An end-to-end radar high-resolution range profile recognition method is proposed based on stacked denosing sparse autoencoder which stacks several denosing sparse autoencoders and uses softmax as the classifier. The training process consists of two steps. The first is layer-by-layer pre-training and the second is fine tuning using the pre-training results for initialisations. The two-step training process makes this model converge faster and more likely to converge to the global optimal point than directly training the joint network. Experimental result shows that the proposed method achieves higher recognition accuracy than state-of-art methods.