基于任务的原始雷达数据压缩评估：经典编解码器在目标检测中失效的预注册研究及原因
Task-Based Evaluation of Raw Radar Data Compression: A Pre-Registered Study of Where Classical Codecs Fail to Preserve Target Detection, and Why
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中文总结 AI 辅助
该研究提出预注册的基于任务的原始雷达压缩评估方法，发现经典编解码器在低于特定比特率时无法维持目标检测效用，明确其失效机制并发布评估工具与预注册轨迹。
中文摘要 AI 辅助
合成孔径雷达（SAR）系统采集的原始I/Q回波数据速率超过下行链路容量；已部署系统采用块自适应量化（BAQ/FDBAQ）进行星上压缩。包括学习型压缩在内的拟议替代方案通常采用图像质量指标（PSNR、SSIM、SQNR）进行评估；但 none 未衡量数据是否仍支持其操作用途。我们引入一种预注册的、基于任务的原始雷达压缩评估方法：在SAR聚焦后，针对双边检测标准对编解码器进行评分——该标准包含恒虚警率（CFAR）检测一致性下限和匹配阈值下的虚警预算，使用在未压缩数据上训练一次的冻结任务模型。在哨兵1号（Sentinel-1）条带地图Level-0数据上，该评估复现了已部署FDBAQ编解码器的工作点（3位BAQ在每个复样本6.12位时维持效用），并测试排除了先前报道的重建晶格伪影。在该场景下，测试的所有经典配置均无法在每个复样本低于4.86位（约13:1）时维持效用，且每次失效都有可识别的机制：原始回波无法提供变换编码增益，而聚焦后的能量集中带来了预期的检测增益（每个复样本2位时检测概率Pd为0.888，对比0.691），但会通过小波振铃转化为虚警；仅检测概率本可认证产生约53倍可容忍虚警数量的编解码器。将相同协议应用于AFRL Gotcha地面动目标指示（GMTI）数据（机载、从未星上压缩），独立复现了两项发现（前沿在每个复样本7.99位）。我们发布了评估工具、具有验证往返可逆性（相对误差约1e-7）的单一去线性调频/聚焦变换，以及完整的预注册轨迹，包括一项撤回的过度主张和小型学习型编解码器的有界阴性结果（作为下限报告）。
英文摘要
Synthetic aperture radar (SAR) systems collect raw I/Q echo data at rates that exceed downlink capacity; fielded systems compress onboard with block-adaptive quantization (BAQ/FDBAQ). Proposed replacements, including learned compression, are typically evaluated with image-quality metrics (PSNR, SSIM, SQNR); none measures whether the data still supports its operational use. We introduce a pre-registered, task-based evaluation methodology for raw radar compression: codecs are scored after SAR focusing against a two-sided detection criterion -- a CFAR detection-agreement floor and a false-alarm budget at matched threshold -- using frozen task models trained once on uncompressed data. On Sentinel-1 stripmap Level-0 data the evaluation reproduces the operating point of the fielded FDBAQ codec (3-bit BAQ sustains utility at 6.12 bits per complex sample), with a previously reported reconstruction-lattice artifact tested for and ruled out. No classical configuration tested sustains utility below 4.86 bits per complex sample (roughly 13:1) on this scene, and each failure has an identifiable mechanism: raw echoes offer no transform-coding gain, while energy concentration after focusing delivers the predicted detection gain (Pd 0.888 vs 0.691 at 2 bits per complex sample) but converts it into false alarms through wavelet ringing; detection probability alone would have certified a codec producing roughly 53 times the tolerable false-alarm count. Applying the same protocol to AFRL Gotcha GMTI data (airborne, never compressed onboard) replicates both findings independently (frontier at 7.99 bits per complex sample). We release the evaluation harness, a unitary dechirp/focus transform with verified round-trip invertibility (relative error ~1e-7), and the complete pre-registration trail, including one retracted overclaim and a bounded negative result for a small learned codec, reported as a lower bound.
