Direction Of Arrival Estimation Method Research Articles

Gridless DOA Estimation Method for Arbitrary Array Geometries Based on Complex-Valued Deep Neural Networks

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform linear arrays or sparse linear arrays. In this paper, we derive the relationship between the element-domain covariance matrix and the angular-domain covariance matrix for arbitrary array geometries by expanding the steering vector using a Fourier series. Then, a deep neural network is designed to reconstruct the angular-domain covariance matrix from the sample covariance matrix and the gridless DOA estimation can be obtained by Root-MUSIC. Simulation results on arbitrary array geometries demonstrate that the proposed method outperforms existing methods like MUSIC, SPICE, and SBL in terms of resolution probability and DOA estimation accuracy, especially when the angular separation between targets is small. Additionally, the proposed method does not require any hyperparameter tuning, is robust to varying snapshot numbers, and has a lower computational complexity. Finally, real hydrophone data from the SWellEx-96 ocean experiment validates the effectiveness of the proposed method in practical underwater acoustic environments.

Research on the vector DOA estimation method with limited number of snapshots

The number of sample snapshots of array signals directly affects the performance of direction of arrival (DOA) estimation methods, and smaller snapshots often cannot represent all the features of array signals. However, in practical applications, owing to short-time abrupt changes, low intensity, large noise interference, and other factors of the target signal, the acoustic vector array sometimes cannot obtain sufficient signal data, making it difficult to achieve accurate DOA estimation. Therefore, this study proposes a transfer-learning-based DOA estimation method for acoustic vector arrays. This method extracts the spatial–temporal features of existing signal data by constructing a pre-trained network model based on a convolutional neural network (CNN) and long short-term memory (LSTM), and transfers the trained model to scenes with limited snapshot data through model fine-tuning, achieving the goal of improving the DOA estimation accuracy under a small number of snapshots. Simulation experiments show that the accuracy and RMSE of the proposed DOA estimation method are superior to those of traditional methods when only 1% of the target data are used. This indicates that the pre-training model based on LSTM and CNN can preserve the effective information of signal data and provides a new solution for the real-time prediction of acoustic vector arrays in scenes with a limited number of snapshots through transfer learning.

Enhanced‐Resolution Learning‐Based Direction of Arrival Estimation by Programmable Metasurface

AbstractDue to its growing importance and wide range of applications, direction‐of‐arrival (DOA) estimation has become a major research topic, particularly in the field of communication systems. While traditional DOA estimation methods rely on antenna arrays and complex algorithms, recent progress achieved in the design and implementation of metasurfaces has proved their effectiveness as promising alternatives. This study presents a distinct approach for DOA estimation that combines the use of a programmable metasurface with deep learning. The programmable metasurface together with a radio‐frequency power detector placed at the focal point, acts as a parabolic reflector antenna with an adjustable pointing direction, which scans the azimuth plane in 5° increments to receive the power level of incoming signals. The collected data is then fed into a pre‐trained multilayer neural network to enable DOA estimation with a resolution of lower than 1° without requiring fine‐tuning of the scanning procedure. This approach ensures accurate and fast estimations, paving the way for advanced solutions in detection and localization for various applications.

Cyclic Beam Direction of Arrival Estimation Method for Ship Propeller Noise

Cyclic Beam Direction of Arrival Estimation Method for Ship Propeller Noise

DOA method based on CSI and improved neural network

Abstract In wireless communication systems, accurate estimation of signal direction has always been a key research area. Channel State Information (CSI) is an important parameter that can be used to estimate the Direction of Arrival (DOA) of a signal. In order to improve the accuracy and robustness of DOA estimation, this study aims to explore a DOA estimation method based on CSI and improved neural networks. Firstly, the role of CSI in localization technology and related theories are detailed. Subsequently, the application of neural network-based fingerprint matching algorithms, including BP neural networks, Whale Optimization Algorithm (WOA), and chaotic mapping techniques, is analyzed. In light of this, an improved BP neural network integrating chaotic mapping and adaptive weighting is proposed to improve the positioning accuracy and convergence rate of the algorithm. Finally, through the collection and training of experimental data, an algorithm proposed in this article achieves a test set accuracy of 96.4286% and a training set accuracy of 82.7243%. The better training effect is obtained, which verifies the efficacy and superiority of the proposed approach.

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

A nonconvex sparse recovery method for DOA estimation based on the trimmed lasso

Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by ℓ2,1-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer-Rao bound.

Motion-guided large aperture ULA to enhance DOA estimation: An inverse synthetic aperture perspective

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution.

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work.

High-accuracy 2D DOA estimation with three parallel sparse nested array

To improve the performance for two-dimensional (2D) direction of arrival (DOA) estimation in the presence of limited antennas in practice, we propose a novel three parallel sparse nested array with extended aperture and reduced mutual coupling. The proposed array consists of three subarrays where locations of antennas have closed-form expressions, to efficiently achieve increased degrees of freedom in difference coarray domain. The unit inter-antenna spacings and the displacements among subarrays are all expanded by D1, D2 or D3 fold for aperture extension and mutual coupling reduction in physical array domain. By exploiting the configuration, the two methods, i.e., BOMP-based and CMT-BCS-based methods, are further explored for high-precision but ambiguous angle estimation, and then the coprime-ness between the displacements is utilized to solve the angle ambiguities. Simulation results demonstrate the superiority of the proposed array and methods for 2D DOA estimation over the existing technologies.

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum.

Accurate direction of arrival (DoA) estimation is paramount in various fields, from surveillance and security to spatial audio processing. This work introduces an innovative approach that refines the DoA estimation process and demonstrates its applicability in diverse and critical domains. We propose a two-stage method that capitalizes on the often-overlooked secondary peaks of the cross-correlation function by introducing a reduced complexity DoA estimation method. In the first stage, a low complexity cost function based on the zero cyclic sum (ZCS) condition is used to allow for an exhaustive search of all combinations of time delays between pairs of microphones, including primary peak and secondary peaks of each cross-correlation. For the second stage, only a subset of the time delay combinations with the lowest ZCS cost function need to be tested using a least-squares (LS) solution, which requires more computational effort. To showcase the versatility and effectiveness of our method, we apply it to the challenging acoustic-based drone DoA estimation scenario using an array of four microphones. Through rigorous experimentation with simulated and actual data, our research underscores the potential of our proposed DoA estimation method as an alternative for handling complex acoustic scenarios. The ZCS method demonstrates an accuracy of 89.4%±2.7%, whereas the ZCS with the LS method exhibits a notably higher accuracy of 94.0%±3.1%, showcasing the superior performance of the latter.

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.

Direction-of-arrival estimation in closely distributed array exploiting mixed-precision covariance matrices

In this paper, we explore a collaborative direction-of-arrival (DOA) estimation technique that utilizes multiple closely spaced subarrays to maximize the potential of distributed arrays while minimizing communication overhead between the subarrays and the processing center. Each subarray computes its self-covariance matrix using the full-precision data and transmits it, along with a one-bit version of the measured data, to the processing center. The processing center generates one-bit cross-covariance matrices between subarrays, which are combined with full-precision subarray self-covariance matrices to create the mixed-precision covariance matrix of the entire array for source DOA estimation. This approach utilizes the full array aperture and all available degrees of freedom of the entire distributed array. To address missing entries in the full covariance matrix, we employ matrix completion, taking into account its Toeplitz and Hermitian structure. For subarrays that are not positioned on the half-wavelength grid, we propose an iterative DOA estimation method to ensure robust DOA estimation performance. Our proposed approach outperforms scenarios where cross-covariance matrices are unavailable or the entire covariance matrix is not interpolated. With the same communication traffic limitation, it demonstrates superiority over schemes that utilize only full-precision data or only one-bit data.

Grid-less wideband direction of arrival estimation based on variational Bayesian inference.

Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed.

DOA estimation and signal sorting methods of multi-baseline polarized interferometer

The density of the signal flow in electronic reconnaissance has developed from tens of thousands of pulses per second earlier to millions of pulses per second currently. To address the problem of rapidly identifying, analyzing, and locating all threat signal sources in the electronic reconnaissance environment. Firstly, the thought of direction of arrival (DOA) estimation followed by signal sorting is investigated, which contributes to achieving the parameter estimation and analysis of all signals in complex electromagnetic environments. Additionally, the DOA estimation method utilizes the signal power received by the array to correctly decouple the spatial phase difference from the polarization phase difference. By exploiting the thought of subspace decomposition, the robustness of the decoupling is improved. Then, the estimation formulas of the DOA and polarization parameters are found. Finally, polarization parameters are used to participate in the signal sorting, which significantly reduces the density of the signal flow faced by the signal sorting method, and improves the real-time and accuracy of signal sorting. The proposed methods achieve microsecond-level DOA estimation and signal sorting for broadband high-density signal flows. Numerical simulation results demonstrate the superiority of the proposed method.

A DOA Estimation Method Based on an Improved Transformer Model for Uniform Linear Arrays with Low SNR

In this paper, the Star-Transformer model is improved to obtain more accurate direction of arrivals (DOA) estimation of underwater sonar uniform linear array (ULA) under low signal-to-noise ratio (SNR) conditions. The ideal real covariance matrix is divided into three channels: real part channel, imaginary part channel, and phase channel to obtain more input features. In training, the real covariance matrix is used under different SNRs. In testing, the covariance matrix of samples in the real environment is used as input. The on-grid form is used to estimate the DOA of multiple signal sources, which is modelled as a multilabel classification problem. The results show that the model can be effective and can still have a good DOA estimation performance under the conditions of trained and untrained SNRs, different snapshots, signal power mismatch, different separation angles, signal correlation, and so on. It shows that the model has excellent robustness.

A New Sparse Bayesian Learning-Based Direction of Arrival Estimation Method with Array Position Errors

In practical applications, the hydrophone array has element position errors, which seriously degrade the performance of the direction of arrival estimation. We propose a direction of arrival (DOA) estimation method based on sparse Bayesian learning using existing array position errors to solve this problem. The array position error and angle grid error parameters are introduced, and the prior distribution of these two errors is determined. The joint probability density distribution function is established by means of a sparse Bayesian learning model. At the same time, the unknown parameters are optimized and iterated using the expectation maximum algorithm and the corresponding parameters are solved to obtain the spatial spectrum. The results of the simulation and the lake experiments show that the proposed method effectively overcomes the problem of array element position errors and has strong robustness. It shows a good performance in terms of its estimation accuracy, meaning that the resolution ability can be greatly improved in the case of a low signal-to-noise ratio or small number of snapshots.

Gridless Underdetermined DOA Estimation for Mobile Agents with Limited Snapshots Based on Deep Convolutional Generative Adversarial Network

Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array (ULA), which leads to grid mismatch issues and difficulty in ensuring accurate DOA estimation with insufficient sampling and in underdetermined scenarios. In order to solve these challenges, we propose a new DOA estimation method based on a deep convolutional generative adversarial network (DCGAN) with a coprime array. By employing virtual interpolation, the difference co-array derived from the coprime array is extended to a virtual ULA with more degrees of freedom (DOFs). Then, combining with the Hermitian and Toeplitz prior knowledge, the covariance matrix is retrieved by the DCGAN. A backtracking method is employed to ensure that the reconstructed covariance matrix has a low-rank characteristic. We performed DOA estimation using the MUSIC algorithm. Simulation results demonstrate that the proposed method can not only distinguish more sources than the number of physical sensors but can also quickly and accurately solve DOA, especially with limited snapshots, which is suitable for fast estimation in mobile agent localization.

An effective DOA estimation method for low SIR in small-size hydrophone array

The estimation ability of traditional direction of arrival (DOA) estimation methods is relatively fragile in small-size hydrophone arrays with limited space. Especially in low signal to interference ratio (SIR), the strong interference signals may submerge some weak signals of interest (SOI) and make DOA estimation difficult in response to this issue. This paper introduces an improved sparse DOA estimation method for practical multi-objective DOA estimation in complex scenarios. The main work is to introduce a noise weight constraint in the sparse iterative covariance process. It leads the algorithm to output sparse peaks and smooth spatial energy spectra and achieve faster fitting while reducing the probability of false peaks. The algorithm can complete DOA estimation of the multi-target reliably without prior information of sources. Then, we propose a fast region grid refinement method based on allocation reconstruction to increase angle resolution. The method increases the accuracy of multi-objective DOA estimation while reducing computational costs. Finally, simulation and experiment have verified the method's effectiveness.

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Traditional direction of arrival (DOA) estimation algorithms typically have poor spatial resolution and robustness. In this paper, we propose a broadband high-resolution DOA estimation method based on the generalized weighted Radon transform (GWRT). The array signal can be converted into the frequency-wavenumber (f-k) domain using the conditional wavenumber spectrum function (CWSF). Then, a linear integral mathematical model for high-resolution DOA estimation is derived by transforming the f-k domain into the azimuth-energy domain using the GWRT. Computer simulation and sea trials were conducted to validate the feasibility and performance of the proposed method. The results obtained indicate that the proposed method yields a lower sidelobe level and can more effectively suppress the output energy in the non-target direction when compared to the conventional beamforming (CBF), steered minimum variance (STMV), and deconvolution (DCV) methods. Further, the proposed method provides improved spatial resolution and robustness in a multi-target environment.