The reconstruction of the three-dimensional structure of synthetic aperture radar (SAR) targets is a hot and difficult issue in the field of SAR. Conventional 3-D reconstruction methods based on 2-D SAR images do not consider the inherent characteristics of SAR imaging such as geometric deformation, overlap, and occlusion, and can only reconstruct simple and regular targets. To address this, we propose a CNN-based SAR 3-D reconstruction method called Multi-aspect SAR Stereo Network (MASS-Net). Our network is an end-to-end deep learning architecture that can automatically complete dense matching among multi-aspect SAR images and calculate the height to obtain height maps by learning prior knowledge. In the network, a feature extractor based on CNN is constructed to extract features from SAR images, which can extract high-dimensional features of 2-D SAR images, and help to capture neighborhood information and overcome the influence of geometric deformation and occlusion. Then a differentiable SAR projection relationship is established to construct a cost volume that includes the differences in multi-aspect image features. This projection relationship ensures ensures the overall differentiability of our pipeline. Meanwhile, the encoding and decoding architecture based on 3-D CNN is utilized to achieve regularization and regression to generate height maps. Finally, we use multi-aspect height maps to construct a dense 3D point cloud of the target. These make MASS-Net efficient and effective. Compared with traditional methods, our method can address issues such as distortion and occlusion, and efficiently reconstruct dense and accurate 3-D point cloud of complex targets. Simulation experiments and actual measurement experiments have been conducted to verify our proposed method.

Bistatic Synthetic Aperture Radar (BiSAR) enables highly flexible configuration, offering broad application prospects. However, existing BiSAR imaging algorithms neglect the complex scattering characteristics of the target, resulting in the loss of target structural information in the imaging results. To enhance the target structural information in imaging results and improve the interpretability of BiSAR images, we first establish the BiSAR echo model based on the target scattering model and analyze the target’s imaging characteristics by incorporating the imaging mechanisms. Subsequently, based on the imaging characteristics, we propose a structure-driven multi-stage BiSAR trajectory planning method (SMTP). This method solves a multi-stage multi-objective optimization problem driven by target scattering characteristics, thereby fully presenting all discernible structural features in the imaging results. Numerical simulation experiments validate the proposed method, demonstrating its ability to recover target structural information. This approach addresses the gap where BiSAR mission planning has largely overlooked target-specific characteristics.

Obtaining the clear contours of ship targets via Synthetic aperture radar (SAR) is extremely valuable for monitoring the sea. Now there are many deep learning imaging methods for ground scenes with good results, but they will face three main challenges when imaging ship targets: 1) The translational and rotational motions of ship targets during travel and due to waves respectively bring spatial invariant and variant errors that are tough to be estimated and compensated, resulting in defocused SAR imaging results; 2) The varying motion of ships demands high generalization ability of the imaging reconstruction network to adapt to the ship targets with sample-wise variant motion parameters; 3) Since ships are noncooperative targets, the accuracy of motion parameter estimation should be evaluated based on image quality, whereas the available SAR image quality assessment functions exhibit limited robustness. To address these issues, this article proposes a deep learning-based SAR imaging framework for ship targets via deep unfolding. Firstly, the motion model and characteristics of ship targets are analyzed, and the SAR imaging model for ship target with complex translational and rotational motion is established. Secondly, an imaging network with high generalization ability is proposed to adapt echoes for imaging under different motion parameters. On this basis, a SAR ship image quality assessment network is proposed to assess the imaging results of SAR ship targets with different focusing qualities. Then, the high-resolution imaging problem of ship targets is regarded as a motion parameter optimization problem, with the image quality assessment results as the objective function. Finally, this problem is optimized to search for the most accurate translational and rotational motion parameter variables of the ship target, achieving error compensation and imaging. The validity of the method is verified though the simulation of point targets and real SAR scenario data.

Bistatic synthetic aperture radar (BiSAR) can provide high-resolution images and effectively observe from various visual angles, enabling automatic target recognition. Estimation of target scattering parameters is one of the important key techniques in target recognition and super-resolution imaging. The compressed sensing(CS)-based methods have been widely used in SAR imaging. However, the hyperparameters of these methods are often difficult to be set to optimal, which leads to inaccurate estimation results. To solve this problem, this paper proposes a BiSAR target scattering parameter estimation method. First, the echo model is established based on the point scattering model and three typical scattering primitives to characterize the different scattering characteristics of different targets. Second the scattering parameter estimation problem is transformed into the optimization problem, and the alternating direction method of multipliers(ADMM) is introduced to estimate target parameters. There are four regularization parameters in the optimization problem, which are automatically set to the optimum by combining with the differential evolution(DE) algorithm. Then the estimated result is obtained by optimization with the optimal regularization parameters. Numerical simulation experiments demonstrate the effectiveness of the proposed method.

Bistatic synthetic aperture radar (BiSAR) parametric imaging can reconstruct the structural information of the target, which is one of the research hotspot for BiSAR imaging. However, compared with monostatic SAR, there are several challenging problems to be faced in terms of BiSAR parametric imaging, such as complex echo models, compute and storage burden, azimuth-dependent phase (ADP). To this end, we first propose the BiSAR parametric echo model, followed by analysis of the characteristics of BiSAR parametric imaging. Based on the abovementioned anaysis, we propose a deep adaptive BiSAR parametric imaging network (DAPI-net), which consists of three parts, namely adaptive dimensionality reduction module, parameter estimation module and image reconstruction module. The main idea of DAPI-net is to employ coarse imaging to determine the approximate range of target parameters and construct a local observation matrix to reduce computational complexity. On this basis, a variant observation matrix learned solver and ADP compensation unit are used to achieve high-precision BiSAR target parameter estimation. Among them, the ADP compensation unit mainly mitigates echo model mismatch caused by ADP and improves the parameter estimation performance. Finally, BiSAR parametric image output is achieved through image dilation. The efficacy of DAPI-net is validated through simulation experiments and microwave anechoic chamber data. The proposed algorithm not only addresses the identified challenges but also advances the state-of-the-art in BiSAR parametric imaging.

The actual detection scenes faced by bistatic synthetic aperture radar (SAR) often have elevation information, which will cause shadow and overlay effects in imaging results. In view of the above problems, this paper proposes an echo generation method of bistatic SAR based on hidden point removal(HPR) operator. In this paper, according to the bistatic configuration, the shadow area is deduced first by using blanking algorithm(HPR operator). On this basis, the visibility of the target in the scene can be determined to obtain the echo. Finally, a simulation using a digital elevation model is conducted to verify the accuracy of the proposed method.

In the application of vehicular MIMO (Multiple Input Multiple Output) radar, it is particularly important to measure and associate the position and velocity of targets. However, traditional methods like 3D-FFT have low angular resolution, and velocity ambiguity resolution is required. Combined with Super-resolution DOA (Direction Of Arrival) algorithm, we propose a multi-dimensional information association method based on target tracking, which uses the labeled GM-PHD (Gaussian Mixture Probability Hypothesis Density) algorithm. This method simultaneously completes the association task about position and velocity and the tracking task, and does not require an additional step on velocity ambiguity resolution. Finally, we use experimental data to verify the effectiveness of the algorithm.

Multi-core digital signal processor (DSP) is widely used in SAR real-time imaging system for its high-speed operation capacity and parallel working ability. The matching of the algorithm and the parallel architecture has great impact on imaging speed of SAR processing. This paper proposed an efficient imaging architecture based on multi-core DSP TMS320C6678. The system implements bistatic polar format algorithm (PFA), using two-dimensional Chirp-Z Transform and one-dimensional interpolation to realize two-dimensional resampling. The experimental results show that the proposed architecture can complete 1024×1024 points echo processing and output a 1024×1024 pixels image within 1.7 seconds.

The joint FPGA and multi-DSP imaging architecture has found widespread use in airborne SAR and satellite-based SAR. However, traditional back-projection (BP) algorithms are not efficient enough for real-time imaging. This paper proposes a back-projection algorithm based on wavenumber-domain spectral splicing (WFBP) for designing an efficient SAR imaging system. A unified polar coordinate system is employed in this work to project the sub-aperture imaging results, which eliminates the need for a large amount of interpolation and multiple projection transformations. Furthermore, spectral shifting is applied to eliminate the effects caused by wavenumber-domain spectral overlap. Experimental results demonstrate that the WFBP algorithm reduces the imaging time by 64% compared to the traditional BP algorithm.

Distributed MIMO Array Radars normally utilize sparse arrays with large equivalent array element spacing, which cannot avoid the generation of grating lobes that cause interference to the received signal. The grating lobes can be suppressed by Phase Coherence Factor (PCF) weighting factor. Regretfully, PCF has problems such as the possibility of suppressing the main lobe as well and the unclear phase resolution of the grating lobes. In this paper, we propose a new weighting factor, namely Embedded Classification Coherence Factor (ECCF), by improving the PCF weighting factor, and propose an embedded superposition method to filter and superimpose the phases of all channels in the BP imaging process. ECCF shows a lower peak side lobe ratio than PCF, and enjoys a superior effect on grating lobe suppression. Simulations are given to verify the performance.

Video Synthetic Aperture Radar (SAR) has received extensive attention in recent years due to its continuous imaging capabilities. Video SAR requires continuous image reconstruction, and there are many redundant operations in the reconstruction process. In order to meet the real-time requirements of video SAR and improve data utilization, we propose a subaperture imaging algorithm based on PFA. First, the echo is divided into multiple subapertures, and coarse focusing is achieved respectively. Then the subaperture echo is subjected to wavenumber mapping to obtain high-resolution high-frame image output. All subaperture data has only been coarsely focused once, and interpolation is not required for wavenumber mapping, which improves the efficiency of image output. Finally, we use experimental data to verify the effectiveness of the algorithm.

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver's maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo's samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo's samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA.