Presence Of Nonuniform Noise Research Articles

Attention based DOA estimation in the presence of unknown nonuniform noise

To solve the problem of performance degradation of the direction-of-arrival (DOA) estimate in non-uniform noise environment, we propose a novel attention mechanism using deep learning technology, namely array covariance attention (ACA). Specifically, to design the ACA, according to the structural characteristics of the covariance matrix, the pooling operation is removed, and one-dimensional convolution kernels are used to aggregate correlation characteristics in two spatial directions. With fully connected layers and non-linear activation layers, the characteristics are then coded into the perceptual attention matrix to improve useful information of the covariance matrix. Furthermore, to achieve a better performance, the integration position of the attention mechanism is also discussed in the network. Finally, a new deep-learning network is created for DOA estimation in the presence of non-uniform noise. The experimental results demonstrate the efficiency and superiority of the proposed network.

A novel variational SBL approach for off-grid DOA detection under nonuniform noise

In this paper, a novel variational sparse Bayesian learning (SBL) approach is proposed for off-grid direction of arrival (DOA) estimation in the presence of nonuniform noise. In the proposed approach, a variational off-grid sparse model over the array covariance vector is first constructed, where the nonuniform noise covariance is modeled into a variational signal vector. At the same time, the grid refining procedure based on the variational off-grid sparse model is re-derived for reducing off-grid error, in which the spatial grid points are linear approximated and iteratively updated. Benefit from the constructed variational off-grid sparse model, the proposed approach does not require a separate estimation for the nonuniform noise variance. Numerous simulation experiments illustrate that the proposed approach has strong robustness to both off-grid error and nonuniform noise, and has superior performance on DOA detection with nonuniform noise.

Wideband DOA Estimation Based on Deep Residual Learning With Lyapunov Stability Analysis

This letter puts forward a new method to estimate the direction of arrivals (DOAs) along with the source number under wideband circumstances. The method constructs a multiple-input-single-output (MISO) framework to make full use of the data from different frequency subbands. Each channel in the framework extracts angular features in parallel through stacked residual blocks. As a result, we can obtain an integrated output vector with smaller error. Because of the well-designed loss function, outputs of the trained model under the proposed framework are self-adjustable with the source number so that the regression of angle values and classification of signal numbers can be completed simultaneously. Moreover, we have proved herein that, if the equivalent matrices of residual blocks are negative definite, then the proposed framework is stable in the sense of Lyapunov. This stability is of great significance to practical applications. Numerical simulations on X-band data demonstrate high estimation accuracy and powerful generalization ability of the proposed scheme in the presence of nonuniform noise.

2D-DOA Estimation for EMVS Array with Nonuniform Noise

Electromagnetic vector sensor (EMVS) array is one of the most potential arrays for future wireless communications and radars because it is capable of providing two-dimensional (2D) direction-of-arrival (DOA) estimation as well as polarization angles of the source signal. It is well known that existing subspace algorithm cannot directly be applied to a nonuniform noise scenario. In this paper, we consider the 2D-DOA estimation issue for EMVS array in the presence of nonuniform noise and propose an improved subspace-based algorithm. Firstly, it recasts the nonuniform noise issue as a matrix completion problem. The noiseless array covariance matrix is then recovered via solving a convex optimization problem. Thereafter, the shift invariant principle of the EMVS array is adopted to construct a normalized polarization steering vector, after which 2D-DOA is easily estimated as well as polarization angles by incorporating the vector cross-product technique and the pseudoinverse method. The proposed algorithm is effective to EMVS array with arbitrary sensor geometry. Furthermore, the proposed algorithm is free from the nonuniform noise. Several simulations verify the improvement of the proposed method.

DOA Estimation in Non-Uniform Noise Using Matrix Completion via Alternating Projection

In this paper, direction-of-arrival (DOA) estimation in the presence of non-uniform noise is investigated. To achieve high estimation accuracy, the DOA estimation problem is solved with the use of a matrix completion approach. The matrix completion via alternating projection algorithm (APA) is firstly utilized to find the noise-free covariance matrix of the signal, which is then used to find the DOA by utilizing the conventional subspace-based method. Numerical simulations are performed to verify the accuracy and effectiveness of the proposed approach.

DOA estimation via ULA with mutual coupling in the presence of non-uniform noise

It is well known that classic direction-of-arrival (DOA) estimation methods yield poor accuracy and low resolution in the presence of mutual coupling and non-uniform noise. To tackle this problem, a DOA estimation method for jointly optimizing noise-free covariance matrix and mutual coupling coefficient is proposed in this paper. Based on the least squares (LS) criterion, the signal subspace and noise-free covariance matrix under mutual coupling can be obtained in an iterative manner. With the determined signal/noise subspace, the mutual coupling matrix can be reconstructed and the covariance one is estimated after mutual coupling compensation. Finally, the noise-free covariance matrix is preprocessed by spatial smoothing strategy and then used to determine the DOAs with the help of traditional DOA estimators. Compared to the conventional DOA estimation methods, simulation results demonstrate that, the proposed method can suppress the non-uniform noise significantly, alleviate the effect of mutual coupling on spatial smoothing technique obviously, as well as improve the DOA estimation performance in the case of coherent signals considerably.

Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise

The uniform white noise assumption is one of the basic assumptions in most of the existing directional-of-arrival (DOA) estimation methods. In many applications, however, the non-uniform white noise model is more adequate. Then the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of non-uniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.

Reconstructing DOA Estimation in the Second-Order Statistic Domain by Exploiting Matrix Completion

It is known that the performance of classical direction-of-arrival (DOA) estimators may be deteriorated considerably in the presence of non-uniform noise and low signal-to-noise ratio (SNR). Focusing on this issue, based on the matrix completion theory, a sparse reconstruction algorithm combining second-order statistical vectors and weighted L1-norm is developed in this paper. In the proposed method, the elastic regularization factor is firstly introduced into the matrix completion model to reconstruct the signal covariance matrix as a noise-free covariance matrix. In what follows, the obtained multi-vector issue associated with the noise-free covariance matrix can be recast as a single vector one by exploiting matrix sum-average operation in the second-order statistical domain. With the constructed single vector, DOA estimation can be complemented by employing the sparse reconstruction weighted L1-norm (WL1) approach. Numerical simulation results show that the proposed algorithm has improved angle estimation accuracy and resolution effectively under low SNR and can suppress the effect of noise non-uniformity significantly.

Robust Sparse Bayesian Learning for Off-Grid DOA Estimation With Non-Uniform Noise

The performance of traditional sparse representation-based direction-of-arrival (DOA) estimation algorithm is substantially degraded in the presence of non-uniform noise and off-grid gap caused by the discretization processes. In this paper, a robust sparse Bayesian learning method is proposed for off-grid DOA estimation with non-uniform noise. In the proposed method, the covariance matrix of non-uniform noise is reconstructed by a modified inverse iteration method. Then, the discrete sampling grid points in the spatial domain are treated as dynamic parameters, and the expectation-maximization algorithm is used to iteratively refine the position of the discretization grid points. This refinement procedure is implemented by solving a polynomial. The simulation results indicate that the proposed method can maintain excellent DOA estimation performance with uniform or non-uniform noise. Furthermore, it can also achieve satisfactory performance under a coarse grid condition.

Off-grid DOA estimation under nonuniform noise via variational sparse Bayesian learning

In this paper, the problem of direction-of-arrival (DOA) estimation in the presence of nonuniform noise is investigated, where the inherent off-grid effects in traditional sparsity-inducing algorithms are also considered. By formulating a sparse signal recovery problem for weighted partial virtual array (PVA) response, we develop a sparse Bayesian learning based method by exploiting joint sparsity between the power distribution of incident signals and the off-grid difference. In our proposed algorithm, a weighted partial covariance vector is obtained through the deliberate projection and decorrelation operations, which facilitates a sparse representation free from the nonuniform noise variances. Meanwhile, a variational Bayesian inference is implemented upon a hierarchical Bayesian learning model with an almost Jeffrey’s prior adopted, which strongly induces the sparsity and involves adaptively tuning sparseness-controlling parameters. Moreover, the proposed method works without the knowledge of the number of sources. Simulation results demonstrate it provides superiority in estimation precision and robustness against nonuniform noise.

Improved sparse Bayesian learning method for direction-of-arrival estimation in non-uniform noise

The estimation of direction-of-arrival (DOA) in the presence of non-uniform noise in array signal processing is investigated in this study. The noise covariance matrix is regarded as an arbitrary diagonal matrix in the estimation. The spatial sparsity of the incident signals in different numbers of snapshots is introduced. The signal power spectrum and noise covariance matrix are then estimated through the improved sparse Bayesian learning (SBL) method. Finally, a high-precision DOA estimation of the incident signals is achieved. The proposed method can be viewed as a further expansion of the SBL-based DOA estimator. Computer simulations show the validity of the proposed method.