Few-Shot HRRP Recognition Based on The Statistical Prototypical Network
Jixi Li, Weiwei Guo, Dongying Li, Feiming Wei, Wenxian Yu · 2024
To mitigate the overfitting in the few-shot high-resolution range profile (HRRP) recognition, we introduce the Mahalanobis based statistical ProtoNet (MSP) with regularization, inspired by the prototypical network (ProtoNet). MSP leverages regularized feature covariance matrix to enhance the ProtoNet’s Euclidean distance metric based on the isotropic Gaussian distribution. Additionally, we propose a simplified MSP, the normalized statistical ProtoNet (NSP) for the faster inference of the statistical ProtoNet. Experiments demonstrate that statistical distance metrics enhance the few-shot recognition performance in scenarios with varying signal-to-noise ratios (SNR) and domain bias.