A multi-norm constraints sparse recovery STAP with coprime sampling structure under array sensor failures
Mingxin Liu, Jialin Zhang, Mingfu Li · Egyptian Informatics Journal · 2026
The coprime sampling structures offer significant advantages in achieving large apertures and high degree of freedom (DOF), but sensor failures can severely impact their space time adaptive processing (STAP) performance. To address this issue, a data completion STAP method based on a multi-norm constrains adaptive alternating direction method of multipliers (AADMM) is proposed. The method first utilizes difference technology to construct virtual space–time snapshots and establishes a multi-norm constrained rank optimization model based on low-rank properties specifically for sensor failure scenarios, aiming to accurately reconstruct missing data and estimate the clutter covariance matrix (CCM). Subsequently, the alternating direction method of multipliers (ADMM) framework is employed to decompose the complex constrained problem into multiple sub-problems for alternating iterative solutions. To overcome the bottleneck of slow convergence in traditional iterative algorithms, an optimal adaptive iteration step-size selection strategy is introduced, which significantly enhances computational efficiency. Validation was conducted through 500 Monte Carlo trials based on simulated side-looking airborne phased array radar data with a clutter-to-noise ratio (CNR) of 30 dB and a signal-to-noise ratio (SNR) of 10 dB. Numerical results demonstrate that in sensor failure scenarios, while the DOF of the traditional STAP method drops to 4 and that of the conventional C-STAP method drops to 9, the proposed AADMM-C-STAP method consistently maintains a virtual DOF of 19, significantly enhancing the system’s clutter suppression capability and target detection robustness.