发表机构
Wrynx Inc（Wrynx公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究测试小型卷积自编码器在GOTCHA数据集上压缩SAR相位历史数据，发现其性能逊于BAQ和KLT，并揭示了评估中的边界伪影与裁剪范围影响，提出了学习雷达压缩的评估协议。
AI 中文摘要
合成孔径雷达（SAR）相位历史数据的星载压缩对带宽要求极高，而分块自适应量化（BAQ）仍是运行标准。我们测试了一个小型卷积自编码器（其编码器置于传感器上）能否在来自AFRL GOTCHA数据集的复数相位历史分块上与BAQ竞争。每种方法都对其传输的所有比特收费，速率以每复数样本比特数（b/cs）报告，检测通过CA-CFAR检测的一对一匹配来评分。自编码器（28,656个编码器参数）在所有速率下均表现不佳。在16 b/cs时，其NMSE为-2.87 dB，而8位BAQ在±3σ裁剪下为-35.5 dB，在调优裁剪范围下为-41.0 dB。它还输给了16×16分块Karhunen-Loève变换（KLT），这是一种局部线性编码器，其编码器成本仅为自编码器的十分之一（-5.39 dB）。在压扩傅里叶域中运行网络有所帮助，但其检测F1分数仍限于33%。证据指向该模型、其归一化及其目标函数，而非学习编码的根本限制。每个分块的数据具有适度的滞后1相干性（|ρ|≈0.3）和分块特定的谱集中度。两个发现涉及评估本身。首先，原始64×64分块上97%的CFAR交叉是零填充检测器的边界伪影。其次，在内部单元上，BAQ的裁剪范围决定检测性能：8位BAQ在调优裁剪下保持69%的F1分数，但在±3σ下仅为17%，且在8 b/cs或更低速率下，自适应FFT阈值化比BAQ保留更多检测。最后，我们提出了一个用于学习雷达压缩的评估协议。
英文摘要
On-board compression of synthetic aperture radar (SAR) phase history is bandwidth-critical, and block-adaptive quantization (BAQ) remains the operational standard. We test whether a small convolutional autoencoder, with its encoder on the sensor, can compete with BAQ on complex phase-history patches from the AFRL GOTCHA collection. Every method is charged for all transmitted bits, rates are reported in bits per complex sample (b/cs), and detection is scored by one-to-one matching of CA-CFAR detections. The autoencoder (28,656 encoder parameters) loses at every rate. At 16 b/cs it reaches -2.87 dB NMSE, against -35.5 dB for 8-bit BAQ with $\pm 3σ$ clipping and -41.0 dB with a tuned clipping range. It also loses to a $16 \times 16$ block Karhunen-Loève transform (KLT), a local linear coder with a tenth of its encoder cost (-5.39 dB). Running the network in a companded Fourier domain helps, but its detection F1 remains bounded at 33%. The evidence points to this model, its normalization, and its objective, not to a fundamental limit of learned coding. Per patch, the data have modest lag-1 coherence ($|ρ| \approx 0.3$) and patch-specific spectral concentration. Two findings concern evaluation itself. First, 97% of CFAR crossings on raw $64 \times 64$ patches are border artifacts of the zero-padded detector. Second, on interior cells BAQ's clipping range decides detection: 8-bit BAQ keeps 69% F1 with tuned clipping but 17% at $\pm 3σ$, and at 8 b/cs or less adaptive FFT thresholding preserves more detections than BAQ. We close with an evaluation protocol for learned radar compression.
