In this letter, a 3-D range migration algorithm for multiple-input multiple-output synthetic aperture radar imaging is proposed. The accurate expression of the signal spectrum is derived by utilizing the spherical wave decomposition. This method compensates for the curvature of the wavefront in wavenumber domain and achieves the 3-D Fourier transform of the reflectivity map through a dimension-reducing accumulation operation. Its fast Fourier transform-based imaging scheme provides high imaging efficiency. In addition, this method does not take the plane wave approximation and can be applied in near-field imaging scenes. Both theoretical analysis and experimental results show that the proposed method can reconstruct the 3-D image as accurately as the backprojection algorithm but with much lower computational load.

Inverse Synthetic Aperture Radar (ISAR) imaging technique of moving target with sparse sampling data has attracted wide attention, due to its ability to reduce the data collection burden. However, traditional low-rank or 2-D Compressive Sensing (CS) based ISAR imaging methods can handle the random sampling or the separable sampling data only. When the specific data collection condition cannot be satisfied, low-rank or 2-D CS based methods cannot provide satisfactory imaging results any more. To remedy this problem, in this paper, we proposed a joint low-rank and sparsity priors constrained model for ISAR imaging with various sparse data patterns. This model is inspired by the facts that the received radar data has a lowrank property and the ISAR image is sparse on the specific dictionary. Two reconstruction algorithms to solve the double priors constrained optimization problem are developed under the alternative direction method of multipliers (ADMM) framework with the help of augmented Lagrange multipliers (ALM). Results on simulation data and real data show that the proposed methods are quite effective in recovering missing samples and focused image, and perform better than the matrix completion based method and the sparse representation based method when dealing with the various kinds of sparse sampling data.

An electronically large planar antenna with wideband illumination retrieves 3-D microwave image with high resolution. Cross multi-input-multi-output (MIMO) topology is commonly used to produce the effective planar array at a reasonable cost and complexity. However, the imaging algorithm for effective monostatic array will cause distortion and dispersion in near range imaging applications. In this paper, an efficient and precise algorithm for near range imaging by cross MIMO array is proposed. The integrals over the transmitting and receiving arrays are carried out in the wavenumber domain to improve efficiency. No approximation is made to further decouple the space and wavenumber variables so the reconstructed image precisely retains both intensity and phase information. The algorithm achieves the same imaging quality as the back-propagation algorithm at the computation complexity of O(N⁴lgN). It can also be implemented to boundary arrays by coherent subarray superposition. Experiments using both simulated and measured data verify its performance. The point spread function and resolution of cross MIMO topology are discussed based on the images reconstructed by this algorithm using measured data.

This paper presents a sequential algorithm for near-field multiple-input multiple-output synthetic aperture radar (MIMO-SAR) imaging. This method can be decomposed into a reconstruction for MIMO array with the range migration technology and a reconstruction for synthesis aperture with the backprojection technology. Different to the range migration algorithm (RMA), the method does not adopt the Fourier transform along the scanning direction; therefore, it can be used for MIMO-SAR systems with arbitrary scanning paths and can perform imaging during the array scanning. In addition, due to its sequential imaging scheme, the proposed method is conducive to parallel processing and reduces the computational memory consumption. Both theoretical analysis and experimental results show that, compared with the RMA and the backprojection algorithm, the proposed method can achieve the same imaging results but with the lowest computational load.

In near-range sparse multiple-input multiple-output (MIMO) array imaging, since grating lobes spread wide and overlap with each other, the grating lobes and the zeros of the transmitting and receiving array cannot cancel each other out. As a result, there are residual grating lobes in imaging results. This article proposes a grating lobe suppression method based on zero migration and multiapodization. The method is equivalent to broadening the zeros of the array. Therefore, the grating lobes are completely suppressed without increasing the redundancy in the array. To realize the zero migration, a novel imaging method named generalized matched filtering is proposed, which can be implemented by an extended backprojection algorithm. For a sparse periodic array (SPA) designed based on the effective aperture concept, the proposed method can further reduce the number of transceivers while suppressing the grating lobes. Both simulated data and measured data verify its performance.

In this article, a grating and sidelobe suppression method for near-range multiple-input multiple-output (MIMO) array imaging is proposed, which achieves grating and sidelobe suppression by reducing the sidelobes of the transmitting and receiving array. The method first utilizes the generalized matched filtering (GMF) for imaging to obtain the imaging results which are separable along the transmitting and receiving array. Then, an image domain phase compensation term is derived to eliminate the spatially variant characteristic of the wavenumber spectrum, and a spatially variant weighting scheme with the adaptive weighting range is proposed to reduce the sidelobes of the transmitting and receiving array, respectively. The proposed method can effectively suppress the grating and sidelobes while maintaining the width of the mainlobe. Both simulated and experimental data verify its effectiveness.