让雷达目标分类更准：加入角度信息可提升7%-10%准确率
High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness
- 训练时显式使用目标朝向角，提升分类模型性能
- 平均准确率提高7%，最高达10%提升
- 用卡尔曼滤波在线估计角度，误差仅5度，实用性强
我们重新研究了在朝向角条件下进行高分辨率距离轮廓（HRRP）分类的问题。以往工作常假设训练时角度信息不完整或推理时不可用，而本文考虑所有训练样本均提供角度信息，并将其显式输入分类器。通过三个数据集和多种条件策略与模型架构的实验，发现单轮廓和序列分类器均能稳定受益于朝向角感知，平均准确率提升约7%，最高可达10%。实际中角度无法直接测量，需估计。我们证明因果卡尔曼滤波可在运行时在线估计角度，中位误差仅为5°，且使用估计角度进行训练与推理仍保留大部分性能增益，支持该方法在真实场景中的可行性。
原文摘要 · Abstract (English)
We revisit High-Resolution Range Profile (HRRP) classification with aspect-angle conditioning. While prior work often assumes that aspect-angle information is incomplete during training or unavailable at inference, we study a setting where angles are available for all training samples and explicitly provided to the classifier. Using three datasets and a broad range of conditioning strategies and model architectures, we show that both single-profile and sequential classifiers benefit consistently from aspect-angle awareness, with an average accuracy gain of about 7% and improvements of up to 10%, depending on the model and dataset. In practice, aspect angles are not directly measured and must be estimated. We show that a causal Kalman filter can estimate them online with a median error of 5{\textdegree}, and that training and inference with estimated angles preserves most of the gains, supporting the proposed approach in realistic conditions.
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