Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF). The sparse SAR imaging technology using compressed sensing (CS) has been developed for enhanced performance, such as superresolution, feature enhancement, etc. More recently, sparse SAR imaging from machine learning (ML), including deep learning (DL), has been further studied, showing great potential in the imaging area. However, there are still gaps between the two groups of methods for sparse SAR imaging, and their connections have not been established.

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.

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.

With the development of modern advanced radar, there are high demands to perform inverse synthetic aperture radar (ISAR) imaging from sparse frequency band (SFB) and sparse aperture (SA) data, respectively. The compressed sensing (CS) based methods are commonly applied to deal with the sparse sampling data, nevertheless, suffering from the inherent problem of basis mismatch on the discrete dictionary. Alternatively, the matrix completion (MC) approaches using the low-rank property are also employed to avoid the discrete error by directly reconstructing the missing data, belonging to the group of grid-free technology. However, this class method can hardly provide satisfactory performance on the type of block sparse sampling data. In this paper, a novel structured low-rank and sparse (SLR+S) algorithm is proposed for high-resolution ISAR imaging with two-dimensional (2D) compressive sampling, which can effectively deal with various types of sparse sampling patterns. In the scheme, the ISAR signal model with 2D compressive sampling is established to integrate three commonly used waveforms, including linear frequency modulation (LFM), sparse stepped LFM and random frequency division modulation. Then, a novel algorithm is proposed for complete sampling data recovery using joint low-rank and sparse constraints. In particular, a structured Hankel formulation is utilized to effectively exploit the latent information of data structure with the enhanced low-rank property. As follows, a fast alternating direction method of multipliers (ADMM) is applied for high-efficiency and high-precision image reconstruction. Compared with traditional CS and MC methods, the proposed algorithm has the capability of dealing with different patterns of 2D compressive sampling. Finally, the experiments using both simulated and measured data are performed to confirm the effectiveness of the proposal.