High-resolution radar imaging with compressive sensing (CS) is significantly important and meaningful in practical applications, such as data collection burden reduction and resource allocation scheduling in a multifunctional radar. The class of matrix completion (MC) methods is a powerful tool to directly reconstruct the missing data to be applied in sparse radar imaging, which can overcome the discrete error drawback of traditional dictionary-based CS methods. In this article, we extend the MC method to tensor completion (TC) with multidimensional data representation, and a novel manifold low-rank and sparse TC (MLRSTC) radar imaging algorithm is proposed for enhanced sparse imaging performance. In the scheme, an attractive tensor radar data model is proposed, and the low-rank tensor property is discovered by capturing the latent and intrinsic data structure in high dimensions. In particular, the low-rankness superiority of the tensor model is confirmed by both the theoretical derivation and experimental analysis. Then, the Kronecker-basis-representation (KBR)-based tensor sparsity model is applied to format the proposed MLRSTC algorithm of sparse radar imaging, which can effectively promote the reconstruction of tensor data with enhanced low-rank property. Meaningfully, the proposed MLRSTC algorithm can work well under the condition of different sparse data sampling patterns. Next, the proposed MLRSTC algorithm is efficiently solved in an iterative manner under the framework of alternating direction method of multipliers (ADMMs) by updating the involved parameters in a closed-form solution. Finally, the experiments using both electromagnetic simulation and measured data are performed to confirm the effectiveness and superiority of the proposed MLRSTC algorithm beyond state-of-the-art (SOTA).

Automotive synthetic aperture radar (SAR) that can achieve long aperture by coherently processing chirps collected by radar mounted on moving vehicle platform shows remarkable superiority in terms of angular/azimuth resolution. To further enhance imaging performance, MIMO technology has been combined with SAR for extended signal-to-noise ratio (SNR), side-lobe level and etc. In this paper, an automotive MIMO SAR image fusion algorithm using tensor decomposition is proposed. In the scheme, the redundancy between MIMO SAR image stacks after compensating phase difference between channels is modeled as the low-rank property of tensor. Then, the low-rank tensor representation is verified and adopted to enhance the image quality of MIMO SAR imaging. Numerical experiments using measured data from an automotive MIMO radar system are carried out. The imaging results obtained using the proposed algorithm show significant improvement compared to single channel SAR and MIMO digital beamforming (DBF) results.

Tomographic synthetic aperture radar (TomoSAR), which can provide three-dimensional (3-D) image of the observed scenes, has become an important technology for topographic mapping, forest parameter estimation, urban buildings modeling and etc. Recently, the developed compressed sensing (CS) and other similar methods have been widely applied for the achievement of super-resolution SAR tomography. However, there always exists inevitable model errors during the mining of scene information, such as discrete gridding on used dictionary and outliers among independent identically distribution (IID) samples, which tends to dramatically degrade the TomoSAR inversion. In this paper, a novel robust gridless CS (RGLCS) algorithm is proposed for high-resolution 3-D imaging of array TomoSAR. In the scheme, the atomic norm minimization (ANM) is used to model the joint-sparsity pattern on elevation distribution between adjacent pixels, which can be treated as gridless CS to avoid the discrete error of the dictionary. Meanwhile, the outliers and disturbances not satisfying the IID elevation distribution are modelled as sparsely distributed spike-noise in the image domain. The proposed RGLCS algorithm has the capability of perfectly separating the outliers and maintaining high-precision height resolution. For efficient solution, a fast alternative optimization is used to solve the objective function to effectively reduce the computational complexity. Next, the post-processing, including point cloud clustering and double-bounce scattering detection & eliminating, are studied to obtain high-resolution 3-D point cloud image. Finally, the experimental analysis using both simulated and measured data are performed to verify the effectiveness of the proposed algorithm. In particular, a practical demonstration using measured airborne array TomoSAR data is presented for urban mapping.

There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.