[Submitted on 25 Sep 2026]
Title:Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA
View a PDF of the paper titled Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA, by Alizishaan Khatri
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Abstract: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 $\pm 3\sigma$ 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 ($|\rho| \approx 0.3$) 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 $\pm 3\sigma$, 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.
Subjects:
Machine Learning (cs.LG); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
Cite as: arXiv:2609.35848 [cs.LG]
(or arXiv:2609.35848v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.35848
arXiv-issued DOI via DataCite
Submission history
From: Alizishaan Khatri [view email] [v1] Fri, 25 Sep 2026 06:36:24 UTC (112 KB)
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