High-precision direction of arrival (DOA) estimation is of great significance for multi-user high-speed communication using millimeter-wave large-scale arrays. To deal with the issues of degraded received signal quality due to wideband effects, reduced signal dimension caused by hybrid structures, and high computational complexity required in multi-user angle estimation, this paper proposes a high-precision DOA estimation method based on wideband signal phase measurement. Firstly, this paper establishes a system model and wideband received signal model for millimeter-wave large-scale arrays with a hybrid structure, and demonstrates the impact of wideband effects. Secondly, this paper derives the Cramer-Rao lower bound (CRLB) for DOA estimation and proposes an optimal training sequence design method that satisfies constant modulus constraints by minimizing CRLB. Subsequently, for single-user scenarios, this paper proposes a gridless high-precision DOA estimation method based on phase measurement reaching CRLB progressively. For multi-user scenarios, an iterative DOA estimation algorithm using the expectation maximization (EM) method is proposed on the basis of single-user DOA estimation to avoid dimension disaster caused by joint estimation and reduce computational complexity. Simulation results verify the effectiveness of the proposed algorithm. When the signal-to-noise ratio (SNR) exceeds 5 dB, the single-user and multi-user estimation algorithms proposed in this paper can progressively achieve CRLB, and the DOA estimation performance surpasses traditional estimation methods, avoiding the impact of wideband effects and signal dimension reduction.

With the continuous construction of new information infrastructures, multi-dimensional array signal processing plays a fundamental role in the filed of radar, wireless communication, remote sensing and so on. Multidimensional array signals contain rich spatial/temporal/frequentiol/polarization parametric information, offering great economic and social values. To deal with the problem of structural information loss inherent in traditional vector/matrix models, the tensor algebra has been adopted to effectively retrieve multi-dimensional signal features. However, as the dimension of signals increases, the tensor signal volume following the Nyquist sampling theorem exponentially expands. Unfortunately, computation resources of the system are approaching the physical limit, resulting in computational overload and high latency. Concerning these issues, the sparse sensing theory has been developed to exploit the spatial sparsity of signals for sub-Nyquist processing. The extension from one-dimensional sparse sensing to multi-dimensional sparse sensing becomes a promising solution to efficient tensor signal processing. Meanwhile, by imposing structured sparse sensing paradigm such as coprime and nested sensing, the performance of the system can be enhanced via augmented coarray signal processing. Thus, to pursue the high economy of multi-dimensional array signal processing, this paper endeavors to the research onStructured Sparse Tensor Signal Processing for Sensor Arrays. In particular, the paper introduces the statistical theory of sub-Nyquist tensor signals. By deriving the augmented coarray tensor model and devising the corresponding strategy of source identifiability enhancement, this theory facilitates Nyquist matching in the virtual domain and underdetermined parameter estimation. Based upon this theory, this paper introduces a coarray tensor completion algorithm for sparse array DOA estimation, exploiting the full information of the discontinuous virtual array to achieve high accuracy and resolution. Meanwhile, this paper introduces a coprime tensor weights optimization algorithm for sparse array beamforming, which yields a beampatten with a sharper mainlobe and lower sidelobes, and increases the output signal-to-interference-plus-noise ratio. Furthermore, this paper introduces a resource-efficient tensorized neural network for robust sparse tensor signal processing, which compensates the performance deterioration for the model-driven methods in non-ideal conditions by efficiently learning tensor signal features.

In order to handle the realistic issue of mutual interference in vehicle-mounted frequency-modulated continuous wave radars, this paper proposes a super-resolution direction of arrival estimation approach based on the third-order cross cumulant. According to the property of non-correlation between the echo signals and the interference signals, the method uses the synchronously sampled array signals to build a third-order cross cumulant matrix, which is then used to obtain a two-dimensional spatial spectrum using the subspace method for joint range and bearing estimation. Simulation results demonstrate that the proposed method can suppress the effect of strong interference and identify multiple targets within 3 degrees. Compared with the existing super-resolution and reconstruction methods, the method has higher angular resolution and estimation accuracy.

Direction of arrival (DOA) estimation uses sensor arrays to identify the direction of sound sources, while traditional DOA estimation methods ignore the sparsity of sound sources in spatial distribution. The penalty function used by current convex sparse DOA estimation methods and non-convex sparse DOA estimation methods do not consider the important scale invariance feature of sparse {\mathit{𝓁}}_{\mathrm{0}} norm, which cannot accurately describe the spatial sparse structure of the sound source, and it is difficult to obtain high DOA estimation accuracy. For this reason, firstly, the scale-invariance norm ratio function is used to approximate the {\mathit{𝓁}}_{\mathrm{0}} norm and characterize the spatial sparse structure of the sound source in this paper; Secondly, aiming at the non-convex property of the norm ratio function, a smooth approximation function is constructed by using the idea of smoothing; Then, the scale-invariant {\mathit{𝓁}}_p-over- {\mathit{𝓁}}_q regularized sparse DOA estimation model is constructed, and meanwhile an optimization algorithm is developed for it. A lot of simulation analysis demonstrate that the proposed algorithm has higher DOA estimation accuracy and better performance under different SNR and snapshot numbers than the popular multi-snapshot DOA estimation algorithm. The analysis results of S5 events in SWellEx-96 sea trial experiment verified the effectiveness of the proposed algorithm.

To address the problems of the existing direction of arrival (DOA) estimation methods of coherently distributed sources, such as huge computational complexity, inferior performance in impulse noise and ineffective decoherence ability, a multimodal DOA estimation method of coherently distributed sources in impulse noise is proposed and the Cramér-Rao bound is derived for DOA estimation of coherently distributed sources in the impulse noise. A multimodal weighted signal subspace fitting equation, employing the weighted norm covariance, is derived firstly to achieve the DOA estimation of coherently distributed sources in the impulse noise, meanwhile, a multimodal quantum bald eagle algorithm is designed to quickly solve the derived equation without quantization error. Simulation results show that the proposed method can achieve the DOA estimation of coherently distributed sources with a small number of snapshots in the impulse noise, and can locate coherent sources without additional decoherence operations. Compared with the existing high precision DOA estimation methods, the proposed method has shorter simulation time and higher estimation accuracy and successful rate, which breaks through the application limitations of the existing coherently distributed source DOA estimation methods and can be popularized and applied in other complex DOA estimation problems.

In order to improve the accuracy of the compressed sensing direction of arrival (DOA) estimation algorithm in polarization-sensitive arrays and avoid the off-grid problem, this paper proposes a gridless direction estimation algorithm using orthogonal dipole arrays based on the theory of atomic norm minimization (ANM). First, the multi-snapshot signal received by the one-dimensional-orthogonal dipole antenna is decomposed into two sub-arrays to be then added up. Then, a semi-definite programming problem is solved to recover a semi-definite Toeplitz matrix containing the information of the incident source, followed by a Vandermonde decomposition of this matrix to recover the DOA information of incoming. At the same time, the covariance matrix vectorization results and the least-squares method are combined to calculate the polarization angle and polarization phase information. By comparing the subspace algorithm and the compressed sensing algorithm under different snapshot numbers and signal-to-noise ratios through simulation experiments, it is proved that the algorithm has a high accuracy of angle measurement.

A new direction-of-arrival (DOA) estimation method is proposed for uncorrelated and coherent mixed signals in the background of colored noise. Firstly, the covariance matrix of the mixed signals is analyzed and processed to eliminate the colored noise. On this basis, the DOA of uncorrelated signals is first estimated by the multiple signal classification (MUSIC) method or the estimation of signal parameters via rotational invariance techniques (ESPRIT) method. Then a new covariance matrix containing only coherent signals without rank deficit is constructed by using the improved spatial difference method. Finally, the DOA of coherent signals is estimated by MUSIC or ESPRIT. The proposed method outperform related methods in estimating DOA of mixed signals, especially coherent signals. Simulation results show the effectiveness of the proposed algorithm.