HRRP target recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder.

Aiming at the problem of feature extraction and recognition in high resolution range profile (IIRRP) target recognition, a recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder (ID SPF-CAE) is proposed in this paper. Firstly, a one-dimensional pooling fusion convolutional autoencoder (ID PF-CAE) is constructed. In the encoding stage, the maximum pooling and average pooling are used to extract different encoding features and fuse them to extract the structural features of IIRRP. Then, multiple ID PF-CAEs are stacked to form ID SPF-CAE. Finally, the network is fine-tuned using label data to realize IIRRP target recognition. And the AdaBound algorithm is used to optimize network training for improving the recognition performance. The experimental results based on the simulated data of the target in the middle part of the trajectory show that the method has strong feature extraction capability, and has high accuracy and robustness for IIRRP target recognition. [ABSTRACT FROM AUTHOR]