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-Lo\`eve 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.