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Radar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid. [ABSTRACT FROM AUTHOR]

Spatially colored noise would lead to performance degradation or even invalidation of multiple input multiple output (MIMO) radar algorithms. For the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar with spatially colored noise, the reason for the failure of the existing algorithms is analyzed. Taking the low-rank property of the noiseless covariance matrix, the sparse feature of the colored noise covariance matrix as well as the multidimensional structure characteristic of the MIMO radar data after matched filtering into consideration, a tensor analysis-based angle estimation is introduced. Firstly, a covariance tensor for the angle estimation is constructed. The colored noise is suppressed via removing the entities that affected by the noise covariance measurement. Then the noiseless covariance tensor is recovered via tensor completion. Thereafter, the factor matrices corresponding to DOD and DOA are achieved via parallel factor (PARAFAC) decomposition. Finally, the DOD and DOA are fitted using least squares algorithm. Simulation results show that the proposed algorithm is not sensitive to the spatially colored noise, and it is free-from the aperture loss. The proposed algorithm performs more accurate estimation performance than the existing matrix and tensor approaches. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the 'holes' that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods. [ABSTRACT FROM AUTHOR]

For radar imaging, a target usually has only a few strong scatterers which are sparsely distributed. In this paper, we propose a compressive sensing MIMO radar imaging algorithm based on smoothed l0 norm. An approximate hyperbolic tangent function is proposed as the smoothed function to measure the sparsity. A revised Newton method is used to solve the optimization problem by deriving the new revised Newton directions for the sequence of approximate hyperbolic tangent functions. In order to improve robustness of the imaging algorithm, main value weighted method is proposed. Simulation results show that the proposed algorithm is superior to Orthogonal Matching Pursuit (OMP), smoothed l0 method (SL0), and Bayesian method with Laplace prior in performance of sparse signal reconstruction. Two-dimensional image quality of MIMO radar using the new method has great improvement comparing with aforementioned reconstruction algorithm. [ABSTRACT FROM AUTHOR]

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work. [ABSTRACT FROM AUTHOR]

A computationally efficient direction‐of‐arrival (DOA) estimation algorithm for monostatic multiple‐input multiple‐output (MIMO) radar based on covariance matrix reconstruction is presented. By reduced‐dimension transformation, the transformed covariance matrix is reconstructed in the Toeplitz structure and then the DOAs are estimated by employing MUSIC. The proposed reduced‐complexity denosing covariance matrix reconstruction approach provides lower computational complexity compared with conventional two‐dimension multiple signal classification (2D‐MUSIC). Moreover, in contrast to the subspace‐based methods, the proposed method can be carried out without knowing the number of targets. Simulation results demonstrate the outperformance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the direction of arrival (DOA) estimation problem for the co-located multiple-input multiple-output (MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing (CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio (SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification (MUSIC) algorithm and other CS recovery algorithms. [ABSTRACT FROM AUTHOR]

Abstract Focusing on the signal to interference-plus-noise ratio (SINR) maximization in colocated multiple-input multiple-output (MIMO) radars, using the transmit covariance matrix (TCM) design of transmit waveforms, we have proposed a TCM R pm with the form of symmetrical Toeplitz matrix, where m is the control parameter to generate different TCM. The main features include (a) full rank, to exploit the waveform diversity advantage of MIMO radar and to further suppress the maximum number of interfering sources, (b) positive semi-definition of sin ((π /2) R pm) to guarantee that R pm can be synthesized with binary phase shift keying (BPSK) waveforms in closed form, (c) higher output SINR level using the R pm with smaller m in the receiver, with the prior knowledge of target and interference locations, (d) lower receive sidelobe levels (SLLs) using the R pm with certain larger m (e.g. m = 1.5 or 2) for the unwanted and unknown sidelobe interference suppression. Simulation results validate the better performance of our proposed TCM compared to the phased-array, omnidirectional MIMO radar, phased - MIMO radar and the recently proposed TCMs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective scheme for angle estimation and array mutual coupling (MC) self-calibration in uniform linear arrays (ULA)-based bistatic multiple-input multiple-output (MIMO) radar. By exploiting the multidimensional inherent structure, the array data is formulated into a trilinear model. The transmit and receive direction matrices are primarily estimated via trilinear decomposition, after which the least square (LS) method is applied to obtain a rough angle estimation. Refined angles are achieved via two one-dimensional local searches. The MC coefficients are obtained via LS by utilizing the estimated angle prior. The proposed scheme can achieve automatic pairing of the estimated angles without any virtual aperture loss, thus it has better angle and MC estimation performance than existing methods. Numerical simulations verify the improvement of our scheme. [ABSTRACT FROM AUTHOR]