Direction Of Arrival Estimation Research Articles

A Survey on Beamspace Millimeter-wave Massive Mimo Systems: An Overview of Open Issues, Challenges, and Future Research Trends

Abstract: The channel sparsity is exploited by transforming the channel into an angular domain from the spatial domain at millimeter wave frequencies in Beamspace Multiple-Input Multiple-Output (B-MIMO) systems. Every beam in B-MIMO corresponds to an individual radio frequency (RF) chain, and thus, beam selection techniques can reduce the RF hardware complexity. RF chain limitation is needed for an ideal beamspace precoding scheme to attain good system performance across the entire bandwidth. New methods of beamspace channel estimation with a small amount of estimation error and low computational complexity are challenging in the beam domain due to the larger antenna arrays deployed. A novel beamspace processing technique to improve the direction of arrival estimation performance for different array configurations with improvement in system performance is desired in a 5G system. A detailed review of the B-MIMO system with its architecture and various beam selection techniques is presented in this paper. The scope of the work is elaborated by incorporating various existing beamspace precoding and channel estimation methods. The paper also presents various beamspace processing schemes for parameter estimation with a focus on the direction of arrival estimation performance.

Direction of arrival estimation using the rotating equatorial microphone

Introduction: Direction of arrival (DOA) estimation of sound sources is an essential task of sound field analysis which typically requires two or more microphones. In this study, we present an algorithm that allows for DOA estimation using the previously designed Rotating Equatorial Microphone prototype, which is a single microphone that moves rapidly along a circular trajectory, introducing DOA-dependent periodic distortions in the captured signal.Methods: Our algorithm compensates for the induced spectral distortions caused by the REM’s circular motion for multiple DOA candidates. Subsequently, the best DOA candidate is identified using two distortion metrics. We verify our approach through numerical simulations and practical experiments conducted in a low-reverberant environment.Results: The proposed approach localizes unknown single-frequency sources with a mean absolute error of 23 degrees and unknown wideband sources with a mean absolute error of 5.4 degrees in practice. Two sources are also localizable provided they are sufficiently separated in space.Conclusion: Whilst previous work only allowed for DOA estimation of a single monochromatic sound source with a known frequency, our DOA estimation algorithm enables localization of unknown and arbitrary sources with a single moving microphone.

Augmented co-prime array for enhanced DOA estimation with hole identification and filling strategies

The co-prime array can effectively reduce mutual coupling, but the holes in difference co-array greatly decrease the uniform degrees of freedom (uDOFs). Hole filling is a considerable strategy to increase uDOFs, and it is critical for direction-of-arrival (DOA) estimation, since the inappropriate hole filling strategy can result in severer mutual coupling. To simultaneously increase DOFs and reduce mutual coupling, a novel generalized co-prime array structure with a sparse hole-filling strategy is proposed in this paper. Firstly, a k-times extended co-prime array with displaced subarrays (kCAD) is proposed, which can provide larger aperture and thus has the potential of increasing uDOFs. Then, in the kCAFD's difference co-array, the closed-form expressions of hole locations are derived. Finally, all the holes in the center of the difference co-array are filled by incorporating two extra subarrays, yielding a greatly increase in uDOFs. This structure is called as a supplementary co-prime array with displaced subarrays (SCAD) in this paper. Numerical simulations show that the proposed SCAD is superior to the traditional existing sparse arrays in terms of the uDOFs and mutual coupling.

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.

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.

Direction‐of‐arrival estimation in coprime linear array using three uniform linear arrays considering mutual coupling

AbstractNowadays, sparse arrays have been a focus for direction‐of‐arrival (DOA). The existing arrays can achieve high degree of freedom (DOF) bigger than the number of sensors when using the spatial smoothing methods. However, the small inter‐element spacing degrades the DOA estimation accuracy when facing severe mutual coupling. In order to alleviate mutual coupling, a coprime linear array using three uniform linear arrays (CLA‐U3) is proposed. The expression of sensor locations is given, and the analysis of DOF is considered. The minimum subarray inter‐element spacing in CLA‐U3 is bigger than that of the existing arrays, which means that CLA‐U3 can be less sensitive to mutual coupling. Moreover, the difference co‐subarrays can fill each other's holes, so CLA‐U3 and other sparse arrays composed of three subarrays have close DOF. Simulation experiments prove the favourable performance of DOA estimation.

Variational Bayesian Gaussian mixture model for off‐grid DOA estimation

AbstractWireless signals are commonly subject to diverse and complex noise interference. The typical assumption of Gaussian white noise often oversimplifies the noise, resulting in reduced accuracy in estimating the direction of arrival (DOA), especially in complex scenarios. To tackle this issue, this paper introduces a new Bayesian model for off‐grid DOA estimation. This model utilizes Gaussian mixture model (GMM)‐based Dirichlet processes (DP) to characterize noise, allowing adaptive adjustments in the number of Gaussian mixture models. Leveraging the factor graph representation of the Bayesian model, a low‐complexity mixed messaging passing algorithm, employing generalized approximate message passing (GAMP) and mean field (MF), is proposed. Simulation results validate the efficacy of the proposed algorithm.

Neural-Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN) based DOA estimation approach for spatial spectrum estimation in multi-source scenarios with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-priori</i> unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low SIR, small sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.

Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array

Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.

Improving estimation performance of compressive sensing‐based multiple‐input multiple‐output radar using electronic beamsteering

AbstractTwo‐dimensional direction‐of‐arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on‐board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple‐input multiple‐output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved.

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.

A low-complexity DOA estimation algorithm using UAMP with Bernoulli-Gaussian prior

In this paper, we design a low-complexity direction-of-arrival (DOA) estimation algorithm based on the unitary approximate message passing (UAMP) and Bernoulli-Gaussian (BG) prior. We first show that the estimation of DOA can be transferred into a sparse signal recovery problem, where we turn to UAMP with damping technique to solve this problem. Furthermore, we assume the BG prior on the sparse vector to be estimated, resulting in the fast estimation of the positions of non-zero elements. Moreover, expectation maximum (EM) is leveraged to automatically learn the BG parameters. Compared to the state-of-the-art UAMP-based algorithm with sparse Bayesian learning (SBL), the proposed approach can achieve the same DOA mean square error (MSE) performance with a much faster convergence speed.

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.

Enhanced Coprime Array Structure and DOA Estimation Algorithm for Coherent Sources.

This paper presents a new enhanced coprime array for direction of arrival (DOA) estimation. Coprime arrays are capable of estimating the DOA using coprime properties and outperforming uniform linear arrays. However, the associated algorithms are not directly applicable for estimating the DOA of coherent sources. To overcome this limitation, we propose an enhanced coprime array in this paper. By increasing the number of array sensors in the coprime array, it is feasible to enlarge the aperture of the array and these additional array sensors can be utilized to achieve spatial smoothing, thus enabling estimation of the DOA for coherent sources. Additionally, applying the spatial smoothing technique to the signal subspace, instead of the conventional spatial smoothing method, can further improve the ability to reduce noise interference and enhance the overall estimation result. Finally, DOA estimation is accomplished using the MUSIC algorithm. The simulation results demonstrate improved performance compared to traditional algorithms, confirming its feasibility.

Robust Sparse Recovery Based Vehicles Location Estimation in Intelligent Transportation System

An architecture for vehicle position estimation is introduced in this paper to tackle the issue of vehicles positioning in traffic congestion of intelligent transportation system (ITS). The introduced architecture is related to three unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), ITS center and the terminal of position estimation, in which the UAV collect data from the ULA, the ITS center store data and the terminal is responsible for executing the corresponding direction of arrival (DOA) estimation algorithm to estimate the vehicle position. In the introduced architecture, DOA estimation is the crucial issue, hence a robust sparse recovery framework based on the optimal weighted subspace fitting is put forward for DOA estimation in the presence of direction-dependent unknown mutual coupling. Firstly, a data model can be obtained by a new transformation approach. Then, a sparse recovery algorithm based on the weighted subspace fitting is used to estimate the desired DOAs. The proposed DOA estimation algorithm can achieve superior performance in both coherent and incoherent signals in the environment of direction-dependent mutual coupling, and this environment often occurs in traffic congestion. Based on the DOA estimation results obtained from the proposed efficient regularized sparse recovery algorithm, the position of vehicles is final estimated by a weighted three-points cross-positioning method. The results of various simulation experiments fully demonstrate the robustness and superiority of the proposed architecture and algorithm.

Improving the Direction of Arrival Estimation Using the Parasitic Subspace Generated by Active-Parasitic Antenna (APA) Arrays

The improvement in Direction of Arrival (DOA) estimation when the received signals impinge on Active-Parasitic Antenna (APA) arrays will be studied in this work. An APA array consists of several active antennas; others are parasitic antennas. The responses to the received signals are measured at the loaded terminals of the active element. The terminals of the parasitic element are shorted. The effect of the received signals on the parasites, i.e., the induced short-circuit current, is mutually coupled to the active elements. Eigen decomposition of the covariance matrix of the measurements of the APA array generates a third subspace in addition to the traditional signal and noise subspaces generated by the all-active antenna receiving array. This additional subspace, the parasitic subspace, is accompanied by very small eigenvalues (approaching zero). Hence, a complete orthogonality between this subspace and the column space of the steering matrix of the array can be obtained. As a result, better resolution in estimating the DOA can be achieved. Several simulations in conjunction with the MUSIC algorithm, which have been conducted in this work, depict that the APA array outperforms the all-active antenna array as a direction finder, regardless of the size of the array, the number of active elements, or the number of measurement snapshots. Furthermore, super-resolution DOA estimation can be achieved when a subset of the parasitic subspace is used as if the measurement were noiseless. Also, the APA array contributes to very small RMSE values over a wide range of S/N of the received signals.

Performance evaluation and direction of arrival estimation in diverse beamforming approaches for enhanced signal reception in wireless communication systems

Performance evaluation and direction of arrival estimation in diverse beamforming approaches for enhanced signal reception in wireless communication systems

Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation

Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation

Multi-target direction-of-arrival estimation of deep models with frame-level permutation invariant training in marine acoustic environment.

Direction-of-arrival (DoA) estimation is an important part in sonar signal processing, providing a reliable foundation for tasks, such as underwater object detection and tracking. Although the deep learning model has powerful data fitting capabilities, accurately estimating the orientation of multiple targets with a single model remains a challenging task. To address this challenge, we enhance the permutation invariant training (PIT) technique and propose two different types of methods: multi-group classification with PIT (MC-PIT) and multi-group regression with PIT (MR-PIT). These two frame-level PIT schemes utilize a single model for both training and testing in multi-target scenarios. Furthermore, we evaluate the performance of MR-PIT and MC-PIT with different network backbones and demonstrate that the frame-level PIT has excellent portability. Compared with the model trained with the general multi-label strategy, simulation experiments show that our proposed methods have better multi-target DoA estimation performance. Finally, when the array configuration of simulated and recorded data are consistent, the model with frame-level PIT can achieve good performance on recorded data even only trained on simulation data.