Semantic Knowledge Guided Hierarchical Classification for Radar Ground Target HRRP Recognition

‍ ‍High-Resolution Range Profile （HRRP） is increasingly recognized as a critical method for ground target identification， reflecting the spatial scattering structure of targets along the radar line of sight. Traditional HRRP identification techniques typically employ hand-crafted features and conventional machine learning classifiers in a flat classification approach， applying a uniform set of preferred features and making a single decision on the final category. However， this approach faces significant challenges in practical applications due to complex target categories， data imbalance， and sensitivity to HRRP postures， often resulting in suboptimal performance. To address these issues， this paper introduces a novel method for radar ground target identification based on a semantically guided hierarchical classification approach. This method adopts a divide-and-conquer strategy， effectively breaking down a complex， fine-grained identification task into multiple， more manageable sub-tasks. It employs a tree structure， jointly constructed using semantic and data-driven information. Each sub-task is matched with a tailored set of optimal features and a local classifier， ensuring a more nuanced and effective approach to target identification. The proposed method has been thoroughly tested and validated using both simulated and real-world data. The experimental results demonstrate the efficacy of this approach in handling ground target identification tasks， significantly enhancing accuracy and robustness compared to traditional methods. This semantically-informed hierarchical approach opens new avenues for advanced ground target identification， providing a robust framework for tackling the inherent complexities in HRRP data.