将雷达高分辨距离图分解为三部分，提出可解释的生成质量评估方法。
MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models
- 将HRRP数据分解为掩码、特征和噪声三部分，基于物理意义设计评估指标。
- 在昂贵数据集上验证，新指标能有效区分生成数据优劣。
- 适合关注雷达目标识别生成模型可解释性与评估的研究者。
高分辨率距离剖面（HRRP）数据在雷达自动目标识别（RATR）中备受关注。随着对基于HRRP分类模型的兴趣增长，利用生成模型填补数据集空缺成为研究热点。然而，生成数据的评估仍是难题，即使对于人脸图像这类显式数据亦然。当前HRRP生成的评估方法依赖分类模型，这些模型被称为“黑箱”，既无法解释生成结果，也无法实现多层级评估。本文聚焦于将HRRP数据分解为掩码、特征和噪声三部分，基于其物理意义提出两种新评估指标。我们利用一个昂贵的数据集，在一项挑战性任务中评估了这些指标，并证明了它们的判别能力。
原文摘要 · Abstract (English)
High-resolution range profile (HRRP ) data are in vogue in radar automatic target recognition (RATR). With the interest in classifying models using HRRP, filling gaps in datasets using generative models has recently received promising contributions. Evaluating generated data is a challenging topic, even for explicit data like face images. However, the evaluation methods used in the state-ofthe-art of HRRP generation rely on classification models. Such models, called ''black-box'', do not allow either explainability on generated data or multi-level evaluation. This work focuses on decomposing HRRP data into three components: the mask, the features, and the noise. Using this decomposition, we propose two metrics based on the physical interpretation of those data. We take profit from an expensive dataset to evaluate our metrics on a challenging task and demonstrate the discriminative ability of those.
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