Compressed sensing radar amid noise and clutter

Adaptive radar processing has been successful in downward looking radars that must detect moving targets in the midst of clutter returns. The performance of compressed sensing techniques in the presence of clutter is explored herein and compared to existing adaptive radar processing methods, including Space-Time Adaptive Processing (STAP), via Monte Carlo exploration of detection performance. Finally, we propose extensions to standard ℓ₁ optimization techniques to account for known interference covariance matrix statistics. These extensions out-perform current compressed sensing techniques, out-perform the fully-sampled, non-adaptive matched filter estimate, and approach the performance level of the fully-sampled STAP estimate.