With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity.

Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation. This paper proposes two deep unfolded gridless DOA estimation networks to resolve the above problem. We first consider the atomic norm-based 1D and decoupled atomic norm-based 2D gridless DOA models solved by the alternating iterative minimization of variables, respectively. Then, the corresponding deep networks are trained offline after constructing the corresponding complete training datasets. At last, the trained networks are applied to realize the 1D DOA and 2D estimation, respectively. Simulation results reveal that the proposed networks can secure higher 1D and 2D DOA estimation performances while maintaining a lower computational expenditure than typical methods.

The lack of independent and identically distributed (IID) training range cells is one of the key factors that limit the performance of conventional space-time adaptive processing (STAP) methods for airborne radar. Sparse recovery (SR)-based and convolutional neural network (CNN)-based STAP methods can obtain high-resolution estimations of the clutter space-time spectrum by using few IID training range cells, so as to realize the clutter suppression effectively. However, the performance of SR-STAP methods usually depends on the SR algorithms, having the problems of parameter setting difficulty, high computational complexity and low accuracy, and the CNN-STAP methods have a high requirement for the nonlinear mapping capability of CNN. To solve these problems, CNNs can be used to reduce the requirements of SR algorithms for parameter setting and iterations, increasing its accuracy, and the clutter space-time spectrum obtained by SR can be used to reduce the network scale of the CNN, resulting in the method proposed in this paper. Based on the idea of deep unfolding (DU), the SR algorithm is unfolded into a deep neural network, whose optimal parameters are obtained by training to improve its convergence performance. On this basis, the SR network and CNN are trained end-to-end to estimate the clutter space-time spectrum efficiently and accurately. The simulation and experimental results show that, compared to the SR-STAP and CNN-STAP methods, the proposed method can improve the clutter suppression performance and have a lower computational complexity.

To tackle the challenges of intractable parameter tuning, significant computational expenditure and imprecise model-driven sparse-based direction of arrival (DOA) estimation with array error (AE), this paper proposes a deep unfolded amplitude-phase error self-calibration network. Firstly, a sparse-based DOA model with an array convex error restriction is established, which gets resolved via an alternating iterative minimization (AIM) algorithm. The algorithm is then unrolled to a deep network known as AE-AIM Network (AE-AIM-Net), where all parameters are optimized through multi-task learning using the constructed complete dataset. The results of the simulation and theoretical analysis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery methods. Furthermore, it maintains excellent estimation performance even in the presence of array magnitude-phase errors.

Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a 3D high-resolution target image with a reduced sampling cost. Firstly, based on the known knowledge of the target and WBI, provided by previous measurements, optimal sparse sampling in the 3D signal domain is conducted to reduce the number of sub-pulses and transceiving antennas by solving an optimization problem. Then, the detection and removal of the interfered signal components are conducted to provide the WBI information for following measurements and the interference-free signal cube for the target imaging process. Finally, by using the tensor-based smoothed L0 algorithm, the 3D high-resolution image of the target is obtained, providing the target information for the next measurement. Based on these three steps, a cognitive sparse imaging loop is formed for MIMO radar under WBI situations. The simulation and experiment results demonstrate the effectiveness and advantage of the proposed methods.

Space-time adaptive processing (STAP) suffers severe performance degradation in airborne bistatic radar, as range-ambiguous clutter exacerbates the inherent clutter non-stationarity. This paper addresses the limitations of conventional beamforming (CBF)-based range-disambiguation methods. We first clarify the core trade-off between range disambiguation accuracy and STAP-accessible degrees of freedom in subarray design. Guided by this, an initial solution utilizing a blocking matrix cascaded with adaptive beamforming (ABF) is proposed. However, in-depth analysis reveals a critical flaw: increasing the subarray size leads to the blocking-matrix-induced noise distortion broadening the mainlobe clutter-suppression notch width. To resolve this, we integrate an innovative zero-phase component analysis (ZCA) whitening technique, developing a blocking matrix-ZCA-ABF joint processing framework. Simulations demonstrate that the proposed method effectively separates range-ambiguous clutter, laying a solid foundation for subsequent non-stationary clutter compensation and STAP suppression. It delivers superior, robust clutter suppression across diverse subarray parameters, narrows the mainlobe clutter-suppression notch, limits the output signal-to-clutter-plus-noise ratio (SCNR) loss to within 3 dB, and maintains target detection power and accuracy simultaneously. Accordingly, it offers reliable technical support for airborne bistatic radar target detection in complex scenarios.