3D SAR Imaging Research Articles

3D sparse SAR imaging based on complex nonconvex penalty function

3D sparse SAR imaging based on complex nonconvex penalty function

3-D Sparse SAR Imaging Based on Complex-Valued Nonconvex Regularization for Scattering Diagnosis

3-D Sparse SAR Imaging Based on Complex-Valued Nonconvex Regularization for Scattering Diagnosis

Volumetric interferometry for sparse 3D synthetic aperture radar with bistatic geometries

AbstractSynthetic Aperture Radar (SAR) renderings in 3D provide additional target information when compared to 2D by separating out features overlaid in height. However, the required 2D SAR aperture, when Nyquist sampled, necessitates large scanning times that would be impractical for most realistic collections. This research has developed a novel volumetric approach to sparse aperture 3D SAR imaging, which is applicable to bistatic SAR near‐field geometries, a generalization of far‐field cases. This approach is first demonstrated in simulation and then applied to a measured scene containing a model vehicle target, producing sub‐Nyquist sampled 3D SAR renderings.

3-D SAR Imaging via Perceptual Learning Framework With Adaptive Sparse Prior

Mathematically, three-dimensional (3D) SAR imaging is a typical inverse problem, which, by nature, can be solved by applying the theory of sparse signal recovery. However, many reconstruction algorithms are constructed by exploring the inherent sparsity of imaging space, which may cause unsatisfactory estimations in weakly sparse cases. To address this issue, we propose a new perceptual learning framework, dubbed as PeFIST-Net, for 3D SAR imaging, by unfolding the fast iterative shrinkage-thresholding algorithm (FISTA) and exploring the sparse prior offered by the convolutional neural network (CNN). We first introduce a pair of approximated sensing operators in lieu of the conventional sensing matrices, by which the computational efficiency is highly improved. Then, to improve the reconstruction accuracy in inherently non-sparse cases, a mirror-symmetric CNN structure is designed to explore an optimal sparse representation of roughly estimated SAR images. The network weights control the hyper-parameters of FISTA by elaborated regularization functions, ensuring a well-behaved updating tendency. Unlike directly using pixel-wise loss function in existing unfolded networks, we introduce the perceptual loss by defining loss term based on high-level features extracted from the pre-trained VGG-16 model, which brings higher reconstruction quality in terms of visual perception. Finally, the methodology is validated on simulations and measured SAR experiments. The experimental results indicate the proposed method can obtain well-focused SAR images from highly incomplete echoes while maintaining fast computational speed.

Three-dimensional SAR imaging with sparse linear array using tensor completion in embedded space

Due to the huge data storage and transmission pressure, sparse data collection strategy has provided opportunities and challenges for 3D SAR imaging. However, sparse data brought by the sparse linear array will produce high-level side-lobes, as well as the aliasing and the false-alarm targets. Simultaneously, the vectorizing or matrixing of 3D data makes high computational complexity and huge memory usage, which is not practicable in real applications. To deal with these problems, tensor completion (TC), as a convex optimization problem, is used to solve the 3D sparse imaging problem efficiently. Unfortunately, the traditional TC methods are invalid to the incomplete tensor data with missing slices brought by sparse linear arrays. In this paper, a novel 3D imaging algorithm using TC in embedded space is proposed to produce 3D images with efficient side-lobes suppression. With the help of sparsity and low-rank property hidden in the 3D radar signal, the incomplete tensor data is taken as the input and converted into a higher order incomplete Hankel tensor by multiway delay embedding transform (MDT). Then, the tucker decomposition with incremental rank has been applied for completion. Subsequently, any traditional 3D imaging methods can be employed to obtain excellent imaging performance for the completed tensor. The proposed method achieves high resolution and low-level side-lobes compared with the traditional TC-based methods. It is verified by several numerical simulations and multiple comparative studies on real data. Results clearly demonstrate that the proposed method can generate 3D images with small reconstruction error even when the sparse sampling rate or signal to noise ratio is low, which confirms the validity and advantage of the proposed method.

Error analysis and 3D reconstruction using airborne array InSAR images

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is able to detect surface deformation and reconstruct a 3D surface model with high precision but requires a long observation period to accumulate the multi-baseline SAR images. The airborne array InSAR system is able to acquire a stack of multi-baseline SAR images in a single acquisition, which significantly improves the 3D modeling capability. However, processing the images obtained by the low-altitude platform using the conventional model will lead to geometric approximation (GA) errors, such as flattened phase error and reference error, which degrade the precision of the 3D reconstruction. In this paper, we quantitatively analyze the error sources of the array-InSAR interferograms and design a hybrid 3D phase unwrapping approach for 3D reconstruction. A hypothesis test is developed to identify the phase ambiguity by comparing the initial solution of the least-square with that of the beam-forming. Three indicators are proposed to identify the reliable arcs, achieving a reliable phase unwrapping. The L1 norm approach is adopted to detect the unwrapping errors during the spatial unwrapping and a two-tie network strategy is used to process the data in the individual blocks from the perspective of global optimization. Furthermore, an iterative scheme is recommended to compensate the geometric approximation errors. The main advantage of the proposed algorithm is the compensation of the GA error and reliable phase unwrapping. The experimental results by both simulated and real SAR data show that the proposed algorithm can eliminate the GA error and provide a viable solution to rapid 3D SAR imaging with an airborne platform.

Comparison Study of Signal Processing Algorithms for 3D SAR Imaging of MM-WAVE GBSAR System

Comparison Study of Signal Processing Algorithms for 3D SAR Imaging of MM-WAVE GBSAR System

3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors

In the research topic of three-dimensional (3D) SAR imaging, the sparsity-enforcing techniques offer promise in shortening sensing time and improving reconstruction accuracy. However, many of them only explore the sparse prior of 3D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors. Wherein, the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3D SAR images with a fast-iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyper-parameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3D SAR images from incomplete echo data.

Precision Downward-Looking 3D Synthetic Aperture Radar Imaging with Sparse Linear Array and Platform Motion Parameters Estimation

The downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3D SAR) has attracted a great deal of attention, due to the ability to obtain three-dimensional (3D) images. However, if the velocity and the yaw rate of the platform are not measured with enough accuracy, the azimuth signal cannot be compressed and then the 3D image of the scene cannot be obtained. In this paper, we propose a method for platform motion parameter estimation, and downward-looking 3D SAR imaging. A DLSLA 3D SAR imaging model including yaw rate was established. We then calculated the Doppler frequency modulation, which is related to the cross-track coordinates rather than the azimuth coordinates. Thus, the cross-track signal reconstruction was realized. Furthermore, based on the minimum entropy criterion (MEC), the velocity and yaw rate of the platform were accurately estimated, and the azimuth signal compression was also realized. Moreover, a deformation correction procedure was designed to improve the quality of the image. Simulation results were given to demonstrate the validity of the proposed method.

Sparse radar imaging based on <italic>L</italic><sub>1/2</sub> regularization theory

Sparse radar imaging seeks to provide high-performance radar imaging technique for observed scenes by using L 1/2 regularization method. This article surveys our systematic explorations and innovative practices for tackling such problem. The key contributions include a new sparse radar imaging theory based on L 1/2 regularization theory, a new sparse radar imaging model based on echo simulation operator, a new sparse radar imaging system evaluate approach based on 3D phase diagram. The synthetic aperture radar (SAR) imaging can be formulated as a L 1/2 regularization problem, which optimizes a quadratic error term of radar imaging. The method can implement SAR imaging effectively at lower sampling rates than Nyquist rate and produce higher quality images with reduced sidelobes. Echo simulation operator is constructed to generate SAR raw data by means of inverse of matched filter-based imaging procedure, instead of the exact observation matrix. The echo simulation operator based model forms a new radar imaging method, which reduces the computational complexity and memory occupation to the same order as matched filter imaging algorithm, and hence, makes L 1/2 regularization reconstruction of large-scale considered scene become possible. 3D phase diagram is employed to analyze and evaluate the performance of sparse radar imaging system. In 3D phase diagram, success rates of signal recovery can be derived from the statistics of relative errors between the ground truth and the recovered signals. The success rates for each combination of SNR, sparsity and sampling ratio are provided to form a three-dimensional phase diagram. The trend of success rates is presented accurately by taking advantage of 3D phase transition boundary. We design the sparse radar prototype and conduct a series of airborne experiments, which demonstrate the applicability and potentials of the sparse radar imaging. 3D phase diagram is used to evaluate the relationship between reconstruction performance and signal-to- noise ratio. The experimental results verity the effectiveness of the L 1/2 regularization method and the feasibility of 3D phase diagram. The sparse radar imaging based on L 1/2 regularization has promising application prospects. Firstly, it could be generalized to deal with 3D-SAR imaging, which achieves better flexibility for system design and super-resolution capability of height dimension for reconstruction property. Then, we review some of the most popular techniques to exploit sparsity, for recovering high-dimensional matrices and higher order tensors from compressive measurements, and further emphasize the broad application prospects of higher order compressive sensing in 3D-SAR imaging.

Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.

Downward looking sparse linear array 3D SAR imaging algorithm based on back-projection and convex optimization

Downward Looking Sparse Linear Array Three Dimensional SAR (DLSLA 3D SAR) is an important form of 3D SAR imaging, which has a widespread application field. Since its practical equivalent phase centers are usually distributed sparsely and nonuniformly, traditional 3D SAR algorithms suffer from low resolution and high sidelobes in cross-track dimension. To deal with this problem, this paper introduces a method based on back-projection and convex optimization to achieve 3D high accuracy imaging reconstruction. Compared with traditional SAR algorithms, the proposed method sufficiently utilizes the sparsity of the 3D SAR imaging scene and can achieve lower sidelobes and higher resolution in cross-track dimension. In the simulated experiments, the reconstructed results of both simple and complex imaging scene verify that the proposed method outperforms 3D back-projection algorithm and shows satisfying cross-track dimensional resolution and good robustness to noise.