AI 中文总结
该研究提出RFCheck方法，通过校准测量审计检测合成射频感知数据的测量一致性失效，验证其在Wi-Fi CSI和FMCW毫米波雷达任务中可有效筛选修复合成数据，提升下游模型性能。
AI 中文摘要
合成射频（RF）感知数据被广泛用于增强无线感知任务，但在匹配的采集条件下，其与真实数据的测量一致性却很少被评估。本文指出了一种测量一致性失效模式：合成样本可能通过面向任务的检查，却偏离了由同一感知链路采集和处理的真实样本的测量行为，这可能引入合成捷径并使下游模型选择产生偏差。我们提出了RFCheck，一种校准的测量审计方法，它使用来自同一采集和预处理链路的保留真实数据作为参考。RFCheck对真实样本进行表征特定测试的校准，并标记出响应超出校准后真实数据范围的合成样本。我们将该审计用于候选筛选和残差修复。我们主要在Wi-Fi信道状态信息（CSI）上验证了RFCheck，该审计检查了延迟域和局部频域结构。实验表明，聚合统计和基于标签的筛选可能会遗漏RFCheck检测到的测量失效。在相同的标签接受规则下，低风险和高风险合成候选表现出不同的下游行为。修复参考将标记率降至10.83%，同时保持平均任务性能。在一项保留提案研究中，修正后进行校准选择，在固定训练预算下生成了一个无标记样本的类别平衡集。我们还将相同的校准原理应用于调频连续波（FMCW）毫米波雷达手势感知，结果表明，即使合成RF感知数据通过了传统任务检查，也可能违反测量一致性，这促使在增强前进行感知意识的诊断和缓解。
英文摘要
Synthetic radio-frequency (RF) sensing data are widely used to augment wireless sensing tasks, yet their measurement consistency with real data is rarely evaluated under matched acquisition conditions. This paper identifies a measurement-consistency failure mode: synthetic samples may pass task-facing checks while deviating from the measurement behavior of real samples collected and processed by the same sensing pipeline, potentially introducing synthetic shortcuts and biasing downstream model selection. We propose RFCheck, a calibrated measurement audit that uses held-out real data from the same acquisition and preprocessing pipeline as the reference. RFCheck calibrates representation-specific tests on real samples and flags synthetic samples whose responses exceed the calibrated real-data range. We use the audit for candidate screening and residual repair. We validate RFCheck primarily on Wi-Fi channel state information (CSI), where the audit examines delay-domain and local frequency-domain structures. Experiments show that aggregate statistics and label-based screening can miss measurement failures detected by RFCheck. Under the same label acceptance rule, low-risk and high-risk synthetic candidates exhibit different downstream behavior. A repair reference reduces the flagged ratio to 10.83% while preserving mean task performance. In a held-out proposal study, correction followed by calibrated selection produces a class-balanced set with no flagged samples under a fixed training budget. We further apply the same calibration principle to frequency-modulated continuous-wave (FMCW) millimeter-wave radar gesture sensing. The results show that synthetic RF sensing data can violate measurement consistency even when conventional task checks are satisfied, motivating measurement-aware diagnosis and mitigation before augmentation.
