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Abstract Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm.

Abstract Space‐time adaptive processing (STAP) struggles to effectively suppress clutter in the heterogeneous clutter environment due to the lack of training samples. In order to enhance clutter suppression performance of STAP, a subspace‐weighted mixed‐norm minimisation approach is given. First, a roughly estimated clutter subspace is obtained using the subspace augment (SA) approach. The weight vector is then designed using the association between the dictionary matrix and the noise subspace, allowing the algorithm to penalise sparse coefficients democratically. Finally, in order to solve the subspace‐weighted mixed‐norm minimisation problem, we derive a fast algorithm based on the alternating direction multiplier method (ADMM) framework. The proposed algorithm does not require iteratively updating the weight vector in contrast to the iterative re‐weighted l1 ${l}_{1}$ (IRL1) algorithm. The simulation results demonstrate the effectiveness of the proposed algorithm in terms of computational efficiency and clutter suppression performance.

Abstract Space‐time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse Bayesian learning algorithm and the adaptive least absolute shrinkage and selection operator algorithm, a hierarchical Bayesian framework with adaptive Laplace priors is proposed. In this paper, a novel method is applied to avoid the high‐dimension matrix inverse operation in the proposed algorithm. Moreover, in order to apply the method in the complex‐valued domain, the complex‐valued signal is split into two independent variables. Then, the sparse recovery problem in the complex‐valued domain can be transformed into the real‐value domain. Simulation experiments show that the proposed algorithm can achieve great clutter suppression performance and also ensure high computational efficiency.