Abstract
A Winner- Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the radar target recognition based on the HRRP. Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoen-coder and deep belief net (DBN), moreover, the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation. In this paper, convolution architecture autoencoder, which is more efficient in spatial feature extraction and sparse coding, is proposed. Furthermore, the half space HRRP samples, which is much more close to the real world situation and is quite different from the free space HRRP samples, is used as the dataset. Half space simulated HRRP data is used to apply the convolutional architecture on the ground target recognition and got an accuracy promotion about 7% compared to conventional vector-based module.
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