Abstract:

The high resolution range profile(HRRP) of radar air target often contains a certain amount of clutter noise, and it is necessary to focus on the influence of noise to carry out air target recognition using HRRP. To address the above issues, an air target HRRP recognition method based on deep residual shrinkage network (DRSN) is proposed, which combines deep residual network, soft thresholding function and attention mechanism, and cross-layer identity connection method is adopted. DRSN can not only avoid the problem of gradient vanishing or gradient exploding caused by too deep layers of the network, which leads to the degradation of the learning ability of the network, but also can effectively filter out the influence of noisy features in the recognition process, so that the model can focus on the recognition of deep features in the target region and improve the recognition ability of the model in the strong noise background. The experimental results show that the proposed method has certain advantages in recognition effect under each signal-to-noise ratio condition compared with other commonly used deep learning models, and the model has strong robustness to noise.

Key words: air target recognition, high resolution range profile (HRRP), deep residual shrinkage network (DRSN), robustness to noise