Gridless sparse recovery STAP algorithm with array amplitude-phase errors for non-uniform linear array

In the continuous angle-Doppler profile, the gridless sparse recovery (SR) space-time adaptive processing (STAP) prevents the off-grid issue of conventional SR-STAP. However, the applicability of the technique is limited to uniform linear array (ULA), whose performance deteriorates significantly owing to the array amplitude-phase errors (AAPEs). To this end, this study proposes a novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs. First, considering the properties of clutter covariance matrix (CCM), the atomic norm minimization (ANM)-STAP model with AAPEs is established for non-ULA. Second, the ANM-STAP is derived as a joint optimization problem for the clutter subspace and AAPEs, which are iteratively updated using the alternating direction method of multipliers. Finally, CCM is reconstructed to design the STAP filter weight vector. Simulation results show that the proposed algorithm is advantageous for non-ULAs and improves the STAP performance in the presence of AAPEs.

Automated summary

A novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs is proposed in this study. By establishing the ANM-STAP model and iteratively updating the clutter subspace and AAPEs, the performance of STAP is improved.