HRRP Few-Shot Target Recognition for Full Polarimetric Radars via SCs Optimal Matching

Most existing radar automatic target recognition (RATR) methods based on high-resolution range profile (HRRP) have been verified to be vulnerable under small sample size conditions, which seriously restricts their real-world promotion and applications. Therefore, enhancing the capability of HRRP few-shot recognition is essential in practical HRRP RATR. In addition, the polarization characteristics of HRRP remain underutilized. Given this, in this article, we propose a full polarimetric radar HRRP (FP-HRRP) few-shot target recognition method from the perspective of optimal matching scatterer components (SCs). Specifically, since heuristic metric methods, such as Euclidean distance are flawed in few-shot classification scenarios, we develop an FP-HRRP few-shot classification method from the perspective of optimal matching between SCs, which transforms the metric problem into solving the optimal solution for a linear programming (LP) model to determine the FP-HRRP relevance. The optimal matching flows between SCs that have the minimum matching cost, which is used to calculate the FP-HRRP distance for classification. To generate the crucial weights of SCs in the LP formulation, we design a cross-similarity weight generation method, which can accurately measure the correlation between SCs and alleviate the adverse impact caused by the intraclass variations arising from azimuth sensitivity and noise. Finally, to integrate intraclass compactness and interclass separation, we proposed a scatterer component cosine loss, further enhancing the separability of feature space. Extensive experiments demonstrate that the proposed method achieves state-of-the-art recognition performance and strong target-aspect robustness for few-shot HRRP recognition with a measured FP-HRRP dataset.