As a key aspect of radio monitoring and positioning, direction of arrival (DOA) techniques have been widely used in wireless communications, radar detection and radio astronomy. DOA estimation can be used to locate a source in space and provide technical support for signal enhancement. However, in complex electromagnetic environments, distributed source models have high complexity; thus, the existing DOA estimation algorithms for distributed sources cannot adapt to application scenarios that require high real-time performance. This letter derives a low-complexity distributed source model and proposes a novel DOA estimation algorithm based on the minimum variance distortionless response criterion. The experimental results demonstrate that the proposed algorithm is superior to some other algorithms in terms of parameter estimation accuracy and source resolution for distributed sources.

Impulsive noise degrades the performance of direction-of-arrival (DOA) estimation methods since the optimization for subspace-like techniques relies on an assumption of Gaussianity. By extending the benefit of cyclic correntropy to the complex field, this letter defines complex cyclic correntropy and deduces some important properties. To reduce the dependence on prior signal knowledge and improve the robustness, a novel kernel function is also derived based on the cumulative distribution function. For cyclostationary signals, an algorithm based on complex cyclic correntropy is proposed to estimate both the central DOA and angular spread for coherently distributed sources in impulsive noise environments. The simulation results verify that the proposed algorithm can achieve better performance than other algorithms in terms of impulsive noise suppression.

Direction of arrival (DOA) estimation methods have been widely and deeply studied in Gaussian noise environments. However, if there is impulsive channel noise, the method performance will significantly decline, and reasonable results may not be obtained. Considering that the high performance of model-driven DOA estimation algorithms requires large arrays and more sample data, this paper proposes a two-stage deep convolutional neural network (TSDCN) algorithm for DOA estimation. The first stage suppresses alpha-stable distributed impulsive noise through an adversarial learning network, and the second stage realizes DOA estimation through a deep convolutional neural network. Simulation and real-world experiments show that the TSDCN outperforms most DOA estimation algorithms in terms of robustness and estimation accuracy in impulsive noise environments.

Blind equalization is widely utilized to eliminate inter-symbol interference in communication system. It is still a challenge to support blind equalization method within non- Gaussian noise environments. Aiming at improving its convergence speed and robustness performance, in this letter, a novel fast blind equalization method is proposed by using bounded nonlinear function (BNF) and quasi-Newton method. Firstly, BNF-based cost function is proposed to effectively eliminate non-Gaussian noise and realize equalization. Next, quasi-Newton method is developed as the iteration method, which can accelerate the convergence speed. Moreover, theoretical analysis is provided to illustrate that the proposed algorithm has a robust convergence performance. Simulation results show that the proposed method enables evident performance improvement in terms of convergence speed and robustness with non-Gaussian noise.

Many approaches have been studied in the field of array signal processing when impulsive noise is modeled with an alpha-stable distribution. By introducing the correntropy, which exhibits a robust statistics property, this paper defines a correntropy-based (COB) operator that provides a powerful mechanism to eliminate the detrimental effect of outliers in alpha-stable distributed noise environments. To improve computational efficiency, we apply the unitary transformation to the COB upper triangular Toeplitz matrix and construct a novel real-valued approximate estimation matrix with a MUSIC-like algorithm. On the basis of guaranteeing the accuracy of the direction of arrival (DOA) estimation, the proposed algorithm also significantly reduces the computational complexity of the calculations. Comprehensive Monte Carlo simulation results demonstrate that the proposed algorithm is more robust than the existing algorithms in terms of the probability of resolution and root mean square error, especially in the presence of highly impulsive noise or low GSNR.