Acoustic Vector Sensor Array Research Articles

Beamforming for Small-Spacing Acoustic Vector Sensor Array Using Optimal Beampattern Synthesis Technique

Traditional low-frequency acoustic senor arrays will always be very large and not easy to be implemented. So it is meaningful to study small-spacing underwater acoustic sensor arrays. The superdirectivity beamforming technique can achieve high array gains for sensor arrays with small apertures, and we study superdirectivity beamforming for small-spacing acoustic vector sensor (AVS) arrays in this letter. We use the beampattern synthesis technique and establish the optimization problem by both considering the approximation of the beampattern to a Dirac delta function and constraint for white noise gain of the beamformer. The superdirectivity beamformers for AVS arrays are then obtained by solving this optimization problem. Simulation results show that the proposed method can achieve high directivity factors for small-spacing AVS arrays. Experimental results of an AVS array with aperture being less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1}\,\text{m}$</tex-math></inline-formula> are also presented in this letter and around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{7.9}\,\text{dB}$</tex-math></inline-formula> array gain is obtained at the frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{200}\,\text{Hz}$</tex-math></inline-formula> .

Off-grid sparse based two-dimensional direction of arrival estimation of acoustic vector sensor array in impulse noise

In order to estimate the direction of arrival (DOA) of acoustic vector sensor, the phased fractional low-order moment (PFLOM) is used to suppress impulse noise. First, based on PFLOM, an off-gird sparse based two-dimensional (2D) DOA estimation model for acoustic vector sensor array is proposed. Then, a three-step alternating iterative Orthogonal Matching Pursuit (OMP) algorithm is introduced to realize the off-grid 2D DOA estimation, and the self-pairing of azimuth and elevation is achieved. Finally, the effectiveness and accuracy of the proposed algorithm under impulse noise and small snapshots are verified by simulation.

Robust Direction Finding via Acoustic Vector Sensor Array with Axial Deviation under Non-Uniform Noise

To minimize the major decline in direction of arrival (DOA) estimation performance for an acoustic vector sensor array (AVSA) with the coexistence of axial deviation and non-uniform noise, a two-step iterative minimization (TSIM) method is proposed in this paper. Initially, the axial deviation measurement model of an AVSA is formulated by incorporating the disturbance parameter into the signal model, and then a novel AVSA manifold matrix is defined to estimate the sparse signal power and noise power mutually. After that, to mitigate a joint optimization problem to achieve the sparse signal power, the noise power and the axial deviation matrix, two auxiliary cost functions, are presented based on the covariance matrix fitting (CMF) criterion and the weighted least squares (WLS), respectively. Furthermore, their analytical expressions are also derived. In addition, to further enhance their prediction accuracy, the estimated axial deviation matrix is modified based on its specific structural properties. The simulation results demonstrate the superiority and robustness of the proposed technique over several conventional algorithms.

Very low frequency three-dimensional beamforming for a miniaturized aperture acoustic vector sensor array.

In this Letter, a three-dimensional beamforming method is proposed for a miniaturized aperture acoustic vector sensor (AVS) array. This method extracts the multipole modes using the AVS array, and then synthesizes the desired beam pattern according to the relation between multipole modes and spherical harmonics. Compared to the spherical harmonics decomposition of the sound field method, the proposed method achieves comparable array gain with higher white noise gain when the distance of adjacent elements d and the wavelength λ satisfy 0.01≤d/λ≤0.2. Experimental results also demonstrate the effectiveness of the proposed three-dimensional beamforming method at very low frequency.

Maximum likelihood DOA estimation based on improved invasive weed optimization algorithm and application of MEMS vector hydrophone array

&lt;abstract&gt;&lt;p&gt;Direction of arrival (DOA) estimation based on Maximum Likelihood is a common method in array signal processing, with many practical applications, but the huge amount of calculation limits the practical application. To deal with such an Maximum Likelihood (ML) DOA estimation problem, firstly, the DOA estimation model with ML for acoustic vector sensor array is developed, where the optimization standard in various cases can be unified by converting the maximum of objective function to the minimum. Secondly, based on the Invasive Weed Optimization (IWO) method which is a novel biological evolutionary algorithm, a new Improved IWO (IIWO) algorithm for DOA estimation of the acoustic vector sensor array is proposed by using ML estimation. This algorithm simulates weed invasion process for DOA estimation by adjusting the non-linear harmonic exponent of IWO algorithm adaptively. The DOA estimation accuracy has been improved, and the computation of multidimensional nonlinear optimization for the ML method has been greatly reduced in the IIWO algorithm. Finally, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) method and Tuna Swarm Optimization(TSO) algorithm, numerical simulations show that the proposed algorithm has faster convergence rate, improved accuracy in terms of Root Mean Square Error (RMSE), lower computational complexity and more robust estimation performance for ML DOA estimation. The experiment with tracking the orientation of the motorboat by Microelectronic mechanical systems (MEMS) vector hydrophone array shows the superior performance of proposed IIWO algorithm in engineering application. Therefore, the proposed ML-DOA estimation with IIWO algorithm can take into account both resolution and computation. which can meet the requirements of real-time calculation and estimation accuracy in the actual environment.&lt;/p&gt;&lt;/abstract&gt;

Augmented Tensor MUSIC for DOA Estimation Using Nested Acoustic Vector-Sensor Array

Nested acoustic vector sensor (AVS) arrays have attracted growing interest, and their performance can be further improved by assembling AVSs with spatially separated (SS) configurations. Following this trend, we propose an augmented tensor-MUSIC (AT-MUSIC) method for SS-AVSs while exploiting the inherent multidimensional structure of the array data. The implications of &#x201C;augmented&#x201D; are twofold: (i) conventional tensor models based on co-centered AVSs are generalized to the case of SS-AVSs, and (ii) available information is fully utilized by judiciously arranging and aggregating the received data. Finally, we show the enhanced performance of AT-MUSIC in simulations.

Two-dimensional DOA estimation method of acoustic vector sensor array based on sparse recovery

In order to solve the Direction of Arrival (DOA) estimation of fast-moving targets in a strong noisy environment, an auto-paired two-dimensional (2D) DOA estimation algorithm architecture based on the sparse recovery of acoustic vector sensor arrays is proposed. The structure of the array signal autocorrelation operator is similar to the 2D compressed sampling model. Based on the structure of the autocorrelation operator, a 2D DOA estimation sparse recovery model for the Uniform Line Array (ULA) of the acoustic vector sensors is proposed. Meanwhile, the modified Compressive Sampling Matching Pursuit (CoSaMP) algorithm is used to realize the auto-paired 2D DOA estimation. The simulation results show that the method proposed in this paper can effectively suppress noise, and can be used to realize 2D DOA estimation of acoustic vector sensor ULA under a low Signal-to-Noise Ratio (SNR) and small snapshots.

Direction finding based on iterative adaptive approach utilizing weighted $$\ell _2$$-norm penalty for acoustic vector sensor array

Direction finding based on iterative adaptive approach utilizing weighted $$\ell _2$$-norm penalty for acoustic vector sensor array

Multi-Maneuvering Sources DOA Tracking With Improved Interactive Multi-Model Multi-Bernoulli Filter for Acoustic Vector Sensor (AVS) Array

To solve the problem of the direction of arrival (DOA) maneuvering in acoustic vector sensor (AVS) array, we propose an interactive multi-model Multi-target Multi-Bernoulli (IMM-MeMBer) two-dimensional (2-D) DOA tracking algorithm. The idea of the IMM algorithm is employed to interactively estimate the predicted sampled particles at the expense of calculating the likelihood function of the predicted particles by pseudo-spectrum of multi-signal classification (MUSIC). The proposed method fully considers various possibilities of target motion, which makes the target state estimation more effective. Moreover, the de-noising and exponential weighting of MUSIC pseudo-spectrum improve the likelihood function of particles, thus enhancing the weight of particles in high likelihood region and making these particles more effective in the subsequent resampling process. Simulation results show that the IMM-MeMBer algorithm has better performance than projection approximation subspace tracking of deflation MUSIC (PASTD-MUSIC), MeMBer, particle filter (PF), and random finite set PF (RFS-PF) in the DOA tracking of maneuvering sources.

An Improved Generalized Inverse Beamforming-Noise Source Localization Method Using Acoustic Vector Sensor Arrays

To resolve the radiated-noise sources of submerged submarines in shallow water, a new source localization method was developed based on generalized inverse beamforming (GIB). We chose a slender cylindrical shell to simulate the submarine, and a horizontal acoustic vector sensor array (AVSA) was applied to conduct source localization. Generally, the location of the horizontal array is close to the water bottom during measurements, and the acoustic reflection by the water bottom is the main factor degrading the performance of the source localization algorithms. To reduce or eliminate the water-bottom reflection, we proposed a new AVSA processing method, which is called combined signal processing (CSP). The proposed CSP method can form a unilateral directivity pattern that suppresses the image source signals without distorting the physical source signals. Subsequently, we introduced two iterative regularization matrices to the GIB algorithm, which contained a priori source information, and repeatedly penalized the iterative solution to improve the localization results. Numerical simulations and experiments were conducted to verify the proposed source localization method, the results of which suggest that it outperforms conventional source localization methods in terms of the dynamic range and spatial resolution of source maps, as well as the localization accuracy.

Sparse Nested Arrays With Spatially Spread Square Acoustic Vector Sensors for High-Accuracy Underdetermined Direction Finding

In acoustic sensing systems, acoustic vector sensor (also known as vector hydrophone for underwater applications) arrays are widely used. Most of the acoustic vector sensor array signal processing methods presume the minimum spacing between two adjacent sensors or sensor components to be within a half-wavelength in order to avoid azimuth-elevation angle estimation aliasing. This would limit the effective array aperture, thereby reducing the potential estimation accuracy. Furthermore, they are unapplicable to the underdetermined scenarios, where the number of sources exceeds that of sensor components. Exploiting the recently proposed nested array concept, we present a new type of nested array, termed as Sparse Nested spatially spread Square Acoustic Vector sensor Array (SNSAVA) to realize underdetermined 2-D direction finding with increased estimation accuracy. In SNSAVA, interspacing of two sensors and two components of a sensor can be spread to be much higher than a half-wavelength so that the effective array aperture will be significantly extended. An unambiguous angle estimation method is further derived to make this fully sparse array configuration practically feasible. Performance studies focused on underdetermined high-accuracy azimuth-elevation angle estimation are provided via numerical examples. The estimation performance for the SNSAVA is also compared with that of the nested acoustic vector sensor arrays, proposed in [33], and with the Cramér-Rao bound.

Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting

A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural low-rank 4-way tensor representation. Thus, rather than estimating the DOAs directly from the raw data, our estimate arises from the unique parametric Canonical Polyadic Decomposition (CPD) of the observations' Second-Order Statistics (SOSs) tensor. By exploiting results from fundamental statistics and the recently re-emerging tensor theory, we derive a consistent blind CPD-based DOAs estimate without prior assumptions on the array configuration. We show that this estimate is a solution to an equivalent approximate joint diagonalization problem, and propose an ad-hoc iterative solution. Additionally, we derive the Cram\'er-Rao lower bound for Gaussian signals, and use it to derive the iterative Fisher scoring algorithm for the computation of the Maximum Likelihood Estimate (MLE) in this particular signal model. We then show that the MLE for the Gaussian model can in fact be used to obtain improved DOAs estimates for non-Gaussian signals as well (under mild conditions), which are optimal under the Kullback-Leibler divergence covariance fitting criterion, harnessing additional information encapsulated in the SOSs. Our analytical results are corroborated by simulation experiments in various scenarios, which also demonstrate the considerable improved accuracy w.r.t. a competing state-of-the-art blind estimate for AVS arrays, reducing the resulting root mean squared error by up to more than an order of magnitude.

Research on Ambiguity Resolution Algorithm by Quaternion Based on Acoustic Vector Sensor

The increase in element spacing can increase the aperture of the array and improve its resolution performance. However, phase ambiguity will occur when the array element interval is larger than the minimum half wavelength of the incident signal. The three acoustic velocity components of the acoustic vector are ingeniously constructed into a new kind of quaternions because of the special structure of the acoustic vector sensor array, and the rough estimation of the direction of arrival (DOA) is obtained using the rotation relationship between the subarray steering vectors corresponding to quaternion data. The rough estimate is used to resolve the phase ambiguity of the spatial phase difference between the array elements, and the high-precision DOA estimation of the signal can be obtained. Simulation results show that the method is effective.

Direction finding via acoustic vector sensor array with non-orthogonal factors

This paper addresses the direction of arrival (DOA) estimation via an acoustic vector sensor (AVS) array in the presence of non-orthogonal factors. To mitigate the estimation bias, which is caused by non-orthogonal factors, a novel DOA estimator is proposed by combining the iterative sparse maximum likelihood-based and maximum a posteriori (ISML-MAP) approaches. First, the two non-orthogonal AVS array models are formulated by introducing a perturbation parameter, based on which the DOA estimation bias is quantified for a single-source scenario. The results show that the non-orthogonal factor has a greater influence on the DOA estimation performance when the velocity sensor located in x-axis is selected as the reference sensor compared to the non-orthogonal AVS array model with the velocity sensor located in y-axis selected as the reference sensor. Then, the DOA of the acoustic source and the non-orthogonal deviation matrix (or angle deviation) are jointly estimated iteratively. In each iteration, three matrix rotation approaches are presented to determine the non-orthogonal deviation matrix. Simulation results demonstrated that the proposed methods achieve better DOA estimation performance than the existing methods for the non-orthogonal AVS array.

Alternating Iterative Adaptive Approach for DOA Estimation via Acoustic Vector Sensor Array Under Directivity Bias

In this letter, an alternating iterative adaptive approach (AIAA) for direction of arrival (DOA) estimation via acoustic vector sensor (AVS) array under directivity bias is proposed. Firstly, the directivity bias parameter is introduced into the signal model. An AIAA is then developed to estimate the sparse signal and the directivity bias matrix iteratively. In each iteration, in order to eliminate the effect of directivity bias, the directivity bias matrix reconstruction method based on the inherent directivity of AVS is presented. Simulation results show that the proposed method effectively improves the accuracy of DOA estimation compared with other state-of-the-art methods.

Estimation of Direction of Arrival of Biological Sound using Acoustic Vector Sensor Array in Shallow Water off Chennai

Mahanty, M.M.; Latha, G.; Sridhar, P.S.S.R., and Raguraman, G., 2020. Estimation of direction of arrival of biological sound using acoustic vector sensor array in shallow water off Chennai. In: Sheela Nair, L.; Prakash, T.N.; Padmalal, D., and Kumar Seelam, J. (eds.), Oceanic and Coastal Processes of the Indian Seas. Journal of Coastal Research, Special Issue No. 89, pp. 52-57. Coconut Creek (Florida), ISSN 0749-0208.The primary objective of this study is to estimate the direction of arrival (DOA) of biological sound by using an acoustic vector sensor array, deployed in the shallow waters off Chennai during the period 30th August-14th September, 2016. A vector sensor measures the scalar acoustic pressure along with acoustic particle velocity in three orthogonal directions. Based on the spectrogram analysis, Terapontidae fish chorus is identified in distinctive patterns associated with daily timing and frequency content throughout the recording. The chorus is species-specific with the spectral peak frequency extended over 0.6-1.5 kHz in each day after dusk, and indicates these sound signals are associated with spawning. The single call duration is about 0.35-0.45 s, and comprised of a series of pulses ranging from 35-40. By considering the single call as a sound source from the recorded Terapontidae fish chorus, the DOA is determined based on conventional beamforming technique with respect to dominant frequency of that particular sound source, and estimated both azimuth and elevation using vector sensor array components. The study clearly shows the application of the vector sensor array, and can be useful to estimate source localization of the biological sound and their movement in the marine environment in future work.

Performance analysis and DOA estimation method over acoustic vector sensor array in the presence of polarity inconsistency

This paper investigates the direction of arrival (DOA) estimation performance in the presence of polarity inconsistency in uniform acoustic vector sensor (AVS) linear array. We analyze the influence of polarity bias on beampattern directivity of the AVS array. The analysis results show that the polarity bias leads to asymptotically biased estimation. Then, the analytical expression for the asymptotic bias based on classical beamforming is derived in the presence of polarity error. Moreover, to improve the DOA estimation performance in the presence of polarity inconsistency, a polarity calibration method is proposed. Numerical simulations reveal the effectiveness and superiority of the proposed calibration method when the polarity error satisfies with the uniform distribution and the normal distribution.

Frequency Invariant Beamforming for a Small-Sized Bi-Cone Acoustic Vector-Sensor Array.

In this work, we design a small-sized bi-cone acoustic vector-sensor array (BCAVSA) and propose a frequency invariant beamforming method for the BCAVSA, inspired by the Ormia ochracea’s coupling ears and harmonic nesting. First, we design a BCAVSA using several sets of cylindrical acoustic vector-sensor arrays (AVSAs), which are used as a guide to construct the constant beamwidth beamformer. Due to the mechanical coupling system of the Ormia ochracea’s two ears, the phase and amplitude differences of acoustic signals at the bilateral tympanal membranes are magnified. To obtain a virtual BCAVSA with larger interelement distances, we then extend the coupling magnified system into the BCAVSA by deriving the expression of the coupling magnified matrix for the BCAVSA and providing the selecting method of coupled parameters for fitting the underwater signal frequency. Finally, the frequency invariant beamforming method is developed to acquire the constant beamwidth pattern in the three-dimensional plane by deriving several sets of the frequency weighted coefficients for the different cylindrical AVSAs. Simulation results show that this method achieves a narrower mainlobe width compared to the original BCAVSA. This method has lower sidelobes and a narrower mainlobe width compared to the coupling magnified bi-cone pressure sensor array.

Sparse representation based direction-of-arrival estimation using circular acoustic vector sensor arrays

Direction-of-arrival (DOA) estimation using circular array has been attracted significant attention in passive sonar system. Recently, the DOA estimation techniques face some challenges such as the small angle spacing of the incident signals, high correlation or coherence between signals, the considerable ambient noise, etc. To solve these problems, this paper proposes a sparse representation (SR) algorithm to estimate the DOAs of signals in the isotropic ambient noise by using the circular arrays composed of acoustic vector sensors (CAVSA). By exploiting the correlation characteristic of the acoustic pressure and particle velocity, two cross-covariance matrices between the acoustic pressure and x-, y-axes particle velocity are first constructed to eliminate the isotropic ambient noise. Then they are stacked to form an augmented cross-covariance matrix, which fully utilizes the direction information of the x- and y-axes particle velocity components. It is observed an interesting fact that when the number of snapshots is limited, the augmented cross-covariance matrix can be divided into the auto-correlation terms of each signal and the cross-correlation terms of any two incident signals coming from different directions. Based on this, the virtual overcomplete and the extra bases between the acoustic pressure and particle velocity are constructed by Khatri-Rao and Kronecker products, respectively. Finally, the SR-based DOA estimation algorithm is derived via the SR of the augmented cross-covariance vector. Simulation and experimental results demonstrate that the proposed method outperforms some existing DOA estimation methods in terms of the spatial spectrum, the estimation accuracy, and the angular resolution.

DOA Estimation of Coherent Signals Based on the Sparse Representation for Acoustic Vector-Sensor Arrays

This paper focuses on the problem of the DOA estimation of coherent signals for the acoustic vector-sensor arrays (AVSAs) in the presence of the isotropic ambient noise. We propose a high-resolution DOA estimation method based on the acoustic intensity principle and the sparse representation technique. First, two cross-covariance matrices are constructed by employing the acoustic pressure and particle velocity components of the AVSA, which eliminates the isotropic noise. Then, in order to fully explore the DOA information of the particle velocity components, an augmented matrix is formed based on the two cross-covariance matrices. We observe an interesting fact that the left singular vector corresponding to the maximum singular value of the augmented cross-covariance matrix is the linear combination of all the signal steering vectors. Based on this fact, a high-resolution DOA estimation algorithm is developed via sparsely representing the left singular vector. This method does not require the prior knowledge of the noise variance or the number of signals to construct the sparse representation model. Simulation and experimental results demonstrate the proposed method outperforms the MUSIC method based on the forward/backward spatial smoothing and some existing sparse representation methods in estimation accuracy and angular resolution, especially in the cases of a low signal-to-noise ratio and/or coherent signals with small angular separations.