SDHC: Joint Semantic-Data Guided Hierarchical Classification for Fine-Grained HRRP Target Recognition

High-resolution range profile (HRRP) is increasingly employed in radar target recognition under intricate ground scenarios. Such scenarios demand recognizing the specific type of a target from a wide range of categories, a task known as fine-grained target recognition (FGTR), which involves numerous and potentially unbalanced categories. To tackle this, we propose a joint semantic-data guided hierarchical classification (SDHC) framework. It consists of a set of local classifiers organized in a tree hierarchy based on the joint semantic-data relationship. It allows the complex FGTR task to be simplified into multiple small-scale subtasks. Specifically, the proposed SDHC method focuses on tree hierarchy construction and local classifier training. We design the tree hierarchy based on a joint semantic-data similarity measure, which quantifies the data similarity between categories and incorporates semantic knowledge constraints. Following this, we deploy hierarchical feature selection on a multidimensional feature set, considering the contribution of features in each local classifier. Experimental results on measured data verify the effectiveness of the proposed method. Moreover, analysis results demonstrate the superiority of the hierarchical approach over flat methods.