One-bit synthetic aperture radar (SAR) imaging has garnered significant interest due to its ability to lower the cost of storing enormous amounts of data during sampling and transmission, as well as the expense of analog-to-digital converters (ADCs). However, existing one-bit SAR imaging methods suffer from high computational complexity and artifacts in the resulting images. To address these problems, the sparse logic regression model (SLR) solved by iterative hard threshold (IHT) is applied to one-bit SAR imaging, and a new SLR-IHT imaging method is proposed. The SLR-IHT method models the one-bit SAR imaging problem as an SLR task and optimizes the solution using the IHT framework. By leveraging the joint sparsity of the real and imaginary components, the proposed method enhances imaging quality while effectively suppressing artifacts. To accelerate computation, the Armijo step size criterion is employed to adjust the step size and support set during the iterative procedure. Moreover, a theoretical investigation into the convergence properties of the proposed method was conducted. Extensive simulations and real data experiments are conducted to evaluate the performance of the SLR-IHT method. The results demonstrate its superiority over existing one-bit SAR imaging techniques in terms of imaging quality and computational efficiency.

Sparse Bayesian learning (SBL) exploits sparse information from a Bayesian perspective by making sparse prior assumptions about the signal. It has good flexibility in modeling sparse signals and can also achieve good estimation results when the number of samples is low. In this letter, we propose a robust adaptive beamforming (RAB) method based on interference-plus-noise covariance matrix (INCM) reconstruction using SBL. This method derives the fixed point iteration formula of the signal under the Laplace prior distribution by maximizing the marginal likelihood function of the data received by the array. Then the sampling position of the coarse grid in the SBL model is regarded as an adjustable parameter, and it is refined through iteration. By using the superiority of SBL in modeling sparse signals, the steering vectors and power of the source signals are precisely estimated, and an accurate INCM is reconstructed to obtain a high output SINR. The only prior knowledge required is imprecise angular sector knowledge of the desired signal (DS). Simulation results demonstrate that our proposed method achieves near-optimal output SINR over a wide range of input SNR and a limited number of snapshots and even achieves good performance in the single snapshot situation.

Current near-field millimeter-wave (MMW) imaging techniques are primarily designed for high-precision quantitative data. Nevertheless, high-precision sampling leads to challenges such as expensive hardware costs and huge data storage requirements. To address these issues, this article introduces a novel near-field MMW sparse imaging method utilizing one-bit measurements. One-bit sampling simplifies the collection and processing of received signals, resulting in a substantial reduction in hardware costs and data storage demands. We model one-bit measurements from a sparsity-driven perspective, based on compressed sensing (CS) theory, and introduce a convolutional reweighted $l_1$ -norm constraint to promote the sparsity of clustered structures commonly found in near-field imaging. Furthermore, to circumvent the computational complexities associated with constructing, storing, and optimizing large-scale matrix-vector multiplications within CS theory, the proposed method utilizes the range migration algorithm (RMA) and its inverse operator as an alternative. The advantages of both CS and matched filtering (MF) approaches in imaging are successfully combined by this strategic integration, greatly reducing the computational and storage costs of using one-bit CS directly. Finally, thorough simulations and real-measured experiments are used to demonstrate the viability and efficacy of the proposed approach, which uses one-bit measurements for imaging.

Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.

One-bit synthetic aperture radar (SAR) imaging technology has garnered significant attention due to its ability to substantially reduce system complexity and data storage requirements. However, noise during the sampling and transmission process can introduce sign flip errors in the echoes, leading to numerous artifacts and clutter in the imaging results, which severely degrade imaging performance. To mitigate this issue, this paper proposes an enhanced one-bit sparse imaging method that leverages two-level structured sparsity based on sparse logistic regression (TSS-SLR). The TSS-SLR method employs a soft coupling mechanism grounded in local structured sparsity, linking each element of the sparse signal with its neighboring elements in the original 2-D image space, which enhances the clustered features of the targets in the imaging results. During the iteration process, traditional thresholding steps struggle to effectively suppress high-intensity artifacts and clutter caused by sign-flip errors. A threshold selection strategy based solely on pixel level may lead to partial recovery or fragmentation of the target. To address this, the TSS-SLR method integrates global structured sparsity constraints through superpixel (SP) segmentation and weighted hard thresholding operations, further suppressing artifacts and clutter. This technique does not alter the actual amplitude of the elements, allowing weak scatterers in the target region to receive higher weights influenced by strong scatterers within the same SP, thereby enhancing retention and improving imaging quality. For efficient model optimization, the alternating direction method of multipliers (ADMM) is utilized, enabling separation and alternating optimization of various constraints. Finally, extensive simulations and real measured data experiments validate the effectiveness of the TSS-SLR method for one-bit SAR imaging in the presence of sign flip errors.