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This letter proposes a novel sparse recovery method of multiple-input and multiple-output (MIMO) radar compressed sensing (CS) imaging algorithms. This method leverages the prior structure of the measurement matrix to judge targets' locations and to estimate the sparsity level in the grid roughly and finally inhabits the emergence of false targets in the imaging figure. Explicitly, the algorithm we propose is inspired by the orthogonal matching pursuit (OMP) algorithm. First, the measurement matrix can be divided into some submatrices by column. Then, we estimate which submatrices do not contain signal components by the algorithms we propose in this literature to achieve the reconstruction of the measurement matrix. Finally, we use the sparse Bayesian learning algorithm and the sparsity adaptive matching pursuit algorithm to recover the target location and scattering intensity. Experiments validate that the reconstruction error of the algorithm we propose is much lower than other sparse recovery algorithms, and targets in the imaging are more obvious than other algorithms.

This paper investigates the problem of mixed far-field (FF) and near-field (NF) source localization using a linear electromagnetic-vector-sensor array with gain/phase uncertainties. Firstly, several special fourth-order cumulant matrices are constructed, such that the shift invariance structure in the cumulant domain can be derived to estimate the DOA and polarization angles of each source at two electromagnetic vector sensors (EMVSs). Then, by computing the determinant of the coefficient matrix, the sources types can be classified with the prior knowledge of the number of both the FF and NF sources. On this basis, the range of NF sources and the DOAs of mixed sources at the phase reference point are captured subsequently. Finally, these estimates can be employed to generate the unknown gain/phase errors. Compared to the existing methods, the proposed one exploits both the spatial and polarization information of sources and provides a satisfactory parameters estimation performance under unknown phase/gain responses. Moreover, it does not need to perform any spectral search and not impose restriction on EMVSs placement, as well as realizes a more reasonable classification of the signal types. Simulations are carried out to verify the effectiveness of the proposed method.

Block sparse Bayesian learning (BSBL) has been widely used in inverse synthetic aperture radar (ISAR) imaging, which significantly improves the imaging performance by exploiting the sparse pattern information of ISAR images. However, the conventional Bayesian learning algorithm has high computational complexity, which hinders its applications to real-time processing of radar imaging. The approximate message passing (AMP) can be used to obtain a low complexity implementation of sparse Bayesian learning (SBL). However, AMP suffers from performance losses and even diverges in the case of high-Doppler resolution ISAR imaging where the measurement matrix can be highly correlated. To solve this problem, we propose a fast pattern coupled SBL ISAR imaging algorithm based on approximate message passing with unitary transformation (UTAMP). First, the estimates of the hyperparameters of sparse vector are obtained through UTAMP based SBL, and then nearest neighbor hyperparameters are coupled and updated for next iteration. With low complexity, the proposed algorithm can effectively exploit the sparse pattern information of ISAR images, and exhibits excellent convergence and imaging performance. Both simulation and real data experiments are carried out to verify the effectiveness of the proposed algorithm.