Direction-of-arrival Estimation Of Coherent Sources Research Articles

Direction of Arrival Estimation of Coherent Sources via a Signal Space Deep Convolution Network

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets.

DOA Estimation for Coherent Sources Based on Uniformly Distributed Two Concentric Rings Array.

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results.

Grid-less DOA estimation of coherent sources based on the covariance matrix recovery

A grid-less direction of arrival (DOA) estimation algorithm via covariance matrix recovery is developed to improve the DOA estimation performance under low signal-to-noise ratio (SNR) for coherent and non-coherent sources. Firstly, we propose to recover the covariance matrix by utilizing both the Hermitian Toeplitz structure of the covariance matrix and the noise statistical characters in the multiple measurement vectors (MMVs). An efficient grid-less DOA estimation algorithm based on the covariance matrix’s first column is also presented to reduce grid mismatch effects. The simulation results show that the proposed DOA estimation algorithm can better recover the covariance matrix than the covariance fitting algorithms with non-coherent and coherent sources. Besides, simulations also verify that the proposed algorithm has a smaller root mean square error (RMSE) and higher estimation probability than the state-of-the-art algorithms in low SNR and small angle separation.

Two-dimensional DOA estimation of coherent sources using two parallel uniform linear arrays

A novel two-dimensional (2-D) direction-of-arrival (DOA) estimation approach based on matrix reconstruction is proposed for coherent signals impinging on two parallel uniform linear arrays (ULAs). In the proposed algorithm, the coherency of incident signals is decorrelated through two equivalent covariance matrices, which are constructed by utilizing cross-correlation information of received data between the two parallel ULAs and the changing reference element. Then, the 2-D DOA estimation can be estimated by using eigenvalue decomposition (EVD) of the new constructed matrix. Compared with the previous works, the proposed algorithm can offer remarkably good estimation performance. In addition, the proposed algorithm can achieve automatic parameter pair-matching without additional computation. Simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

A Reduced Complexity Approach to IAA Beamforming for Efficient DOA Estimation of Coherent Sources

We address the 2D direction-of-arrival (DOA) estimation problem in scenarios with coherent sources. More specifically, we adopt beamforming solutions based on the iterative adaptive approach (IAA) recently proposed in the literature. The motivation of such adoption mainly comes from the excellent behavior these beamformers provide in scenarios with coherent sources. Nonetheless, these strategies suffer from a prohibitive computational complexity, especially in 2D scenarios. In order to alleviate the, we propose two reduced-complexity (RC) versions of the IAA and IAA based on maximum likelihood (IAA-ML) algorithms. The proposed beamformers are referred to as IAA-RC and IAA-ML-RC and provide similar results to those obtained with their original counterparts. Computational complexity, however, is further reduced. Numerical results presented in the paper show that the computational burden can be decreased by a 52% with our proposed solutions in the considered scenarios.