Performance Analysis of Phase and Amplitude Gradient Method for DoA Estimation using Underwater Acoustic Vector Sensor

The acoustic vector sensor (AVS) array is a powerful tool for direction of arrival (DOA) estimation of underwater targets. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. A novel signal self-nulling based DOA estimation method is proposed. Firstly, achieve the crude estimation of the desired DOA using the traditional algorithms. Secondly, set the observation angular interval around the crudely estimated DOA. Thirdly, make the observation direction vary in the observation angular interval, and for each imaginary DOA, calculate the amplitude response of the minimum variance distortionless response (MVDR) beamformer. Finally, the pseudo spatial spectrum is achieved. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

This paper mainly introduces a novel estimation method for direction of arrival (DOA) estimation. Recently, direction-of-arrival (DOA) estimation has been widely employed in wireless communication networks, electromagnetic (EM) environment management, and radar tracking. However, traditional DOA estimation algorithms and systems are based on multiple digital receivers with expensive hardware. To overcome this challenge, we propose a novel DOA estimation method based on a space-time coding method and an easy-configurable antenna array. With this programmable L-shape antenna array and space-time coding method, a two-dimensional DOA estimation is realized.

Direction of arrival (DOA) estimation performance severely degrades for the reasons that the perturbation from their assumed nominal complex gain values in practical sensor system. In this paper, to mitigate the effects of the uncertainty perturbation, a two-step method for joint DOAs and sensor errors estimation is proposed. In the first step, convex optimization and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm penalties are used to conduct a second-order cone framework to estimate DOAs roughly under the assumption that all sensor gains are identity. Second, we calibrate the perturbed gains with the DOAs estimated by the first step. The iterative process of the DOAs and gains estimation is taken until convergence. Furthermore, a refinement procedure is addressed to alleviate the effects of the grid limitations to spatial locations. In the present method, no auxiliary calibrated sensors or co-operation signal source is required. And the two-step method can handle both uncorrelated signals and coherent signals, it is also a high resolution method and robust in the condition of low signal-to-noise ratio (SNR). Several aspects are taken into consideration, and numerical experimental results are given to show the advantages of the new method for the DOA estimation.

DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.

Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework

Passive sonar arrays are often used to find submerged vessels by measuring their radiated signal. Acoustic vector-sensor (AVS) arrays, which are normally towed by the ship at the ends of long cables, have been proven to be very effective for target direction of arrival (DOA) estimation. The flow noise, arising from the flow of water over the vector-sensor or its housing when the array is towed, is often a major source of interference to the towed array as it is usually directly coupled to the array sensing elements. In this paper, we obtain a closed-form expression for the spatial correlations of all the components of an AVS array in a flow-noise field. We apply the noise correlation model to DOA estimation using a uniform linear AVS array beamforming. The flow-noise impact on the beamforming capabilities is evaluated via numerical simulations.

Design, development, fabrication and evaluation of the dynamics of a graphene based underwater acoustic vector sensor: A simulation and experimental study

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

A study on the application of Toeplitz Approximation Method(TAM) on direction of arrival(DOA) estimation is described in this paper. The proposed algorithm provides high resolution direction finding capability at low signal noise ratio(SNR), and it can be applied to the narrowband coherent sources DOA estimation, it is also efficient for the wide-band coherent sources DOA estimation, it has low calculation and doesn't decrease the antenna effective aperture. Simulation results show that the method is simple and efficient.

A novel approach combining agile beam switching with deep learning to enhance the speed and accuracy of Direction of Arrival (DOA) estimation for millimeter-wave (mmWave) phased array systems with low-complexity hardware implementations is proposed and evaluated. Traditional DOA methods requiring direct access to individual antenna elements are impractical for analog or hybrid beamforming systems prevalent in modern mmWave implementations. Recent agile beam switching techniques have demonstrated rapid DOA estimation, but their accuracy and robustness can be further improved via deep learning. BeamSeek addresses these limitations by employing a Multi-Layer Perceptron (MLP) and specialized data augmentation that emulates real-world propagation conditions. The proposed approach was experimentally validated at 60 GHz using the NSF PAWR COSMOS testbed, demonstrating significant improvements over a correlation-based method across various Signal-to-Noise Ratio (SNR) levels. Results show that BeamSeek achieves up to an 8° reduction in average estimation error compared to this baseline, with particular advantages in noisy channels. This makes it especially suitable for practical mmWave deployments in environments characterized by multipath interference and hardware constraints.

The high resolution of DOA(direction of arrival) estimation could be obtained by using the min-norm algorithm. In this paper, the expression of the min-norm spatial spectrum based on acoustic vector-sensor(AVS) linear arrays was presented and simulation study was carried out. Results of simulations indicated that left/right ambiguity could be removed and better performance for DOA estimation was obtainable when dealing with sources close to endfire than using pressure hydrophone linear arrays, and the interelement spacing was allowed to exceed the half-wavelength upper limit. A three-element vector hydrophone linear array with two meters interspace was designed. The AVS experiment was carried out at Songhua Lake in Jinlin Province. Experimental results show a high resolution tracking of targets can be obtained using the min-norm algorithm.

We study the performances of several computationally efficient and simple techniques for estimating direction of arrival (DOA) of an underwater acoustic source using a single acoustic vector sensor (AVS) in shallow water. Underwater AVS is a compact device, which consists of one hydrophone and three accelerometers in a packaged form, measuring scalar pressure and three-dimensional acceleration simultaneously at a single position. A very controlled experimental setup is prepared to test how well-known techniques, namely, arctan-based, intensity-based, time domain beamforming, and frequency domain beamforming methods, perform in estimating DOA of a source in different circumstances. Experimental results reveal that for almost all cases beamforming techniques perform best. Moreover, arctan-based method, which is the simplest of all, provides satisfactory results for practical purposes.

In the field of array signal processing, direction of arrival (DOA) estimation is a prime area of research. DOA estimation and adaptive beamforming (ABF) are the main issues in smart antennas, which dynamically find the direction of desired and interfering users and finds the optimum weights for beamforming. There are numerous spectral and eigen structure algorithms for estimating the direction of narrow band sources. However, in a complex multipath channel environment, received signals from different directions are fully or partially correlated, which prevents the applications of high resolution techniques to estimate the direction of incoming signals. Maximum likelihood (ML) is an efficient technique for DOA estimation in a low signal to noise ratio (SNR) and coherent channel environment. In this paper, we use particle swarm optimization (PSO) for estimating ML solution by optimizing complex non linear multimodal function over a high dimensional space in linear arrays. PSO-ML estimator has been compared with conventional DOA estimation techniques in uncorrelated, partially correlated and coherent channel environment. The performance of PSO-ML estimator and conventional algorithms are analyzed in varying partially correlated channel environment. The simulation results demonstrate that PSO based estimator gives superior statistical performance.

Traditional orthogonal matching pursuit (OMP) algorithms for direction of arrival (DOA) estimation suffer from poor angular resolution and noise suppression. In this paper, we analyze the reason why the OMP algorithm has difficulties in resolving closely separated DOAs and conclude that it lies in the rules of support detection. Moreover, we propose a solution to this problem via developing the connection between the sparse reconstruction class algorithm and the subspace algorithm from the structure of the redundant dictionary. Based on the framework of the matching pursuit (MP) algorithm, the effective information of the signal and noise subspaces is integrated, and a noise subspace reprojection orthogonal matching pursuit (NSRomp) algorithm for DOA estimation is proposed. By adopting signal subspaces to reconstruct the original signal, the proposed NSRomp can reduce both the influence of noise on the selection of the support set and the computing time. By implementing the minimum norm method to optimize the noise subspace into a vector, which corrects the selection rules of the support set during each iteration, the angular resolution of the proposed algorithm is improved. From the simulation results, when the signal to noise ratio (SNR) is lower than or near 0, the angular resolution can be improved by > 15° using OMP algorithms to by > 5° using the proposed NSRomp algorithm.

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.