CVPCNN: Conditionally variational parameterized convolution neural network for HRRP target recognition with imperfect side information

Ting Chen, Shuai Guo, Xinwei Deng, Penghui Wang, Jun Ding, Hongwei Liu, Yinghua Wang

Abstract: Highlights•To the best of our knowledge, this is the first study to consider the azimuth estimation errors of the HRRP and relate them to deep neural network parameter control.•We proposed two new convolutional kernel parameterization methods, VPCONV and extended CVPCONV, which embed SI into deep network by using it to parameterize the convolutional kernel. This enables direct control of the network structure. Simultaneously, variational encoding is introduced in the reparameterization process to generalize the azimuth estimation error using a heteroscedastic Gaussian distribution with varying mean and variance. Notably, VPCONV and CVPCONV exhibit good transferability and can be used as plug-and-play modules.•Based on CVPCONV, we designed a lightweight network CVPCNN, which can make full use of the azimuth of the HRRP, decouple the tight coupling between the azimuth and target HRRPs, and extract discriminative characteristics that are more relevant to the current environment (azimuth). This helps obtain a more robust and accurate recognition performance. In addition, the CVPCNN introduces a sample-dependent kernel attention mechanism based on parallel convolution to achieve efficient inference, even with increased model complexity.•The effectiveness of the proposed model was tested in a target recognition task using a three-class measured aircraft HRRP dataset. The experiments show that the CVPCNN model, with just three convolutional layers, demonstrate optimal performance on this dataset. This represents a 4 % improvement over the standard CNN model under identical experimental conditions and model structure. Significantly, the CVPCNN maintain good recognition performance under non-ideal conditions, such as small sample and incomplete HRRP attitudes.