Study on the Directivity and Signal Processing of Hybrid Acoustic Vector Sensor Array

Through in-pressure sensor array signal processing, the signal arrival angle can be estimated by the estimation of signal parameters via rotational invariance techniques (ESPRIT) if we regard the whole array as two sub-arrays with the same structure. The estimation angles arrived at by ESPRIT have a left-right ambiguity because of the inverse trigonometric function operation. An acoustic vector sensor array (AVSA), which measures particle velocity and pressure at the same time and point of the acoustic field, offers significant potential. The traditional ESPRIT algorithm can also be used for an AVSA with left-right ambiguity. A new algorithm with omnibearing and an high accuracy based on ESPRIT using an AVSA was proposed in this paper. An analytic particle velocity, which composes by acoustic vector sensor x-axis and y-axis velocities, embeds the signal angle in its phase. The signal broadside can be known by the ESPRIT based on analytic particle velocity and pressure sub-arrays. The estimation angle containing broadside information can be used as the driving angle for combined particle velocity. According to the signal broadside, an average cross-variance matrix of pressure and combined velocity was constructed. The signal angle can be estimated by ESPRIT based on this new combined cross-variance matrix. Theoretical analysis and computer simulations show that the proposed algorithm has no left-right ambiguity and also has higher accuracy than the analytic particle velocity algorithm in the case of low signal-to-noise ratio (SNR). The algorithm is based on pressure and particle velocity combined processing, which can suppress interference better than existing algorithms in isotropic noise. The algorithm is also based on the analytic particle velocity ESPRIT, which has no left-right ambiguity. So the new algorithm has better performance than the existing algorithms.

Coherent signal-subspace processing of acoustic vector sensor array for DOA estimation of wideband sources

This paper presents a strategy called the alternating iterative minimization method (AIMM), aimed at enhancing the precision of direction of arrival (DOA) estimation when utilizing an acoustic vector sensor array (AVSA) with unknown swing deviation elements (SDEs). The AVSA model with unknown SDEs is formulated by incorporating the swing deviation parameter. Later, to estimate the swing deviation matrix (SDM) and the sparse signal power by using the alternating iteration method, the auxiliary cost functions with respect to SDM and the sparse signal power are formulated based on the regularized weighted least squares (RWLS) and regularized covariance matrix fitting (RCMF) criteria. Furthermore, their analytical expressions have also been quantified. In order to mitigate the effect of unknown SDEs on the accuracy of DOA estimation, any sub-time segment (STS) in the dataset is selected as the reference to convert the received data of different STS into the reference STS using the estimated SDM. The simulation and experimental outcomes conclusively represent the effectiveness of the suggested TSIM approach using AVSA in handling unknown SDEs.

In this paper, based on the root-MUSIC algorithm for acoustic pressure sensor array, a new self-adapting root-MUSIC algorithm for acoustic vector sensor array is proposed by self-adaptive selecting the lead orientation vector, and its real-valued formulation by Forward-Backward(FB) smoothing and real-valued inverse covariance matrix is also proposed, which can reduce the computational complexity and distinguish the coherent signals. The simulation experiment results show the better performance of two new algorithm with low Signal-to-Noise (SNR) in direction of arrival (DOA) estimation than traditional MUSIC algorithm, and the experiment results using MEMS vector hydrophone array in lake trails show the engineering practicability of two new algorithms.

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

Acoustic sensor arrays used in wind tunnels for acoustic measurements are affected by channel and background noise which can be greater than the signal sources. The noise is mainly related to the wind tunnel parameters, interactions between the wind and the wall, and the acoustic sensor. This study combines channel signal filtering with processing of the cross spectral matrix (CSM) diagonal elements to recover the signal of interest from the channel noise with an extended Kalman filter to track the phase difference of the background noise to optimize the coherent background noise. Simulations show that this method effectively reduces the number of side lobes, suppresses the side lobe level, reduces the influence of noise on the beamforming results, and greatly improves the imaging by the acoustic sensor array. 摘要 风洞中使用声传感器进行声学测量, 通道噪声和背景噪声不可避免, 其强度甚至超过信号源。噪声的存在与风洞工作参数、风与壁面的相互作用以及声传感器选择有直接关系。该文根据噪声产生的原因, 通过通道滤波与处理谱交叉矩阵对角线元素相结合来优化通道噪声, 设计扩展Kalman滤波器渐进跟踪背景噪声相位差来优化相干背景噪声。仿真结果表明：所提出的方法可以有效减少旁瓣数量、抑制旁瓣水平, 减少噪声对波束形成结果的影响, 改善声传感器阵列的成像效果。

Most underwater target tracking algorithms are based on noisy bearing-only measurements. The bearing measurements are actually bearing estimates obtained from acoustic field measurements made by an array of sensors placed on a manoeuvrable platform. In this paper, we investigate the problem of tracking a manoeuvring target in the ocean directly from the acoustic field measurements. The use of acoustic field measurements eliminates the need for the use of a manoeuvrable platform, and also eliminates the intermediate step of bearing estimation. We consider the vector acoustic field measurements by an array of acoustic vector sensors (AVS), which are more informative than the scalar acoustic field measurements by an array of acoustic pressure sensors (APS). We model the target dynamics by a piecewise-constant turn rate model. The resultant nonlinear estimation problem is solved using the Gaussian approximation filtering approach. Within this framework, we consider the quasi-Monte Carlo Kalman filtering (QMC-KF) algorithm that employs an advanced numerical integration procedure to solve the multi-dimensional integrals appearing in the filtering process. We also derive the posterior Cramer-Rao lower bound (PCRLB) on the root mean square error (RMSE) of the sequential Bayesian estimator, and show that the PCRLB for acoustic field measurements is lower than that for bearing-only measurements. Simulation results indicate that acoustic field measurements yield a significantly better performance than bearing-only measurements, the QMCKF performs better than other deterministic filters, and an AVS array provides a much better performance than an APS array.

An acoustic vector sensor array consists of multiple sound pressure microphones and particle velocity sensors. A pressure microphone usually has an omni-directional response, yet a particle velocity sensor is directional and usually has a response pattern as a figure of eight. Currently, acoustic vector sensor arrays are under investigation for far field source localization and visualization. One of the major practical issues in these applications, however, is to determine the accurate position, orientation and complex (phase and amplitude) sensitivity of each sensor within the array. In this study, a new calibration method is verified with experiments. The method determines all the crucial parameters based on a limited number of measurements with a reference sensor and multiple sound sources located at known locations. The experiments are performed in an anechoic room. The results are promising.

The motivation of this work is to investigate the source number detectability by an oversampled or undersampled linear acoustic vector sensor (AVS) array. Sufficient conditions that ensure the extension of existing source number estimators and beamforming techniques from the acoustic pressure sensor (APS) array to the AVS array are established based on the linear independence of steering vectors of the AVS array, an issue closely related to the number of sources whose parameters [e.g., the direction of arrival (DOA)] can be uniquely determinable. The upper bound on the detectable number of sources with distinct DOAs under oversampled and undersampled array configurations is derived, where the difference of definitions of the distinct DOAs between the APS array and the AVS array is clarified. The number constraint on detectable sources with distinct spatial aliasing DOAs using the undersampled AVS array is also investigated. Under these conditions, advantages of eigenvalue-based source number estimators using AVS arrays are demonstrated, and the minimum description length (MDL) estimator is extended to the AVS array as an illustration to verify the established theories.

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

Differential line arrays (DLAs) hold significant potential for practical signal enhancement, but DLAs are not capable of realizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i>th-order fully steerable function. To overcome this problem, this paper introduces a novel fully steerable differential beampattern with controllable nulls to address the limited steering capability of conventional differential beamformers based on linear arrays, which enables asymmetric beampattern design. The proposed differential beamformer is implemented through McLaughlin series expansion to approximate the constructed target beampattern and establishes the relationship between controllable nulls and the weights of beamformer, thereby enforcing the null constraint. The main contributions of this paper are as follows: 1) A fully steerable differential beamforming design method is proposed, which not only achieves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i>th-order fully steerable function but also enables more controllable interference suppression; 2) Validated through simulation comparisons, the proposed method based on linear acoustic vector sensor arrays (LAVSAs) mitigates the limitation of conventional linear arrays, which fail to achieve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i>th-order fully steerable function, and exhibits superior directivity factor performance. Finally, the effectiveness of the proposed method is demonstrated by experimental results.

This paper provides a novel acoustic vector sensor array architecture for superdirectivity at low frequencies. It has a uniform 3 by 3 rectangular geometry of intersensor spacing less than 0.04 wavelength over its operation frequency band, with an up to 10.4 dB of directivity index in cylindrically isotropic noise. This is realized through a mode domain approach, which accesses the acoustic modes by fitting certain multipole models to the wavefield observations. Field experiments show that this is a practical solution of implementing a miniaturized vector sensor array with superdirectivity.

An intelligent algorithm was developed based on backpropagation artificial neural network for the acoustic emission source localization. The established and trained methods of the algorithm were stated with the time difference of arrival detected by a fiber optic acoustic emission sensor array and the coordinates of acoustic emission source. The response characteristic of fiber optic acoustic emission sensor was calibrated with the commercial piezoelectric ceramic transducer (PZT) acoustic emission sensor, which provided that the fiber optic acoustic emission sensor was better suited to detect the low frequency of stress wave than the PZT sensor. Four fiber optic acoustic emission sensors were deployed in a square array in an aluminum plate for comparisons between different algorithms of source localization. Comparison results of acoustic emission source location provided that the intelligent algorithm improved the accuracy by reducing the nonlinear errors. For the anisotropic materials, a sensor array deployed in a diamond pattern was adopted. The velocities of stress waves in orthogonal directions were measured as the basic performance for both algorithms of source localization. Four sensors were integrated into a carbon fiber–reinforced polymer plate as a perfect structure for locating the acoustic emission source impacting on its surface. The experiment results provide that the maximum error is only 6.3 mm using the intelligent algorithm.

An vector sensor is a compact device which simultaneously measures the scalar pressure p(t, r/spl I.oarr//sub 0/) and the gradient of the pressure, /spl nabla/p(t, r/spl I.oarr//sub 0/), at some measurement point r/spl I.oarr//sub 0/. Thus, a three-dimensional (3D) vector sensor produces four time series outputs, which can be processed to provide some degree of spatial filtering and a direction of arrival (DoA) estimate to an target. Historically, two-dimensional (2D) vector sensors have been used in several Navy systems, like the DIFAR sonobuoy (AN/SQQ-53 series) and the AN/WLR-9 intercept receiver (which uses the multimode hydrophone). These devices have made important contributions to the Navy sonar community, where it is desirable to obtain an accurate azimuthal DoA estimate for bearing to a low-frequency target from a single point in space. Other researchers have used the outputs of vector sensor to estimate intensity vector (watts/m/sup 2/). In the literature, these intensity-related vector sensors have been referred to as intensity probes or acoustic watt-meters. In this paper, we consider 3D vector sensors that uses three orthogonal underwater accelerometers with a scalar pressure sensor of hydrophone packaged in a single housing. The sensors will be referred to as low-frequency vector sensors since they were optimized for the 50 Hz to 2 kHz band. We will show that these vector sensors can produce a frequency-independent spatial response over the aforementioned band. Specifically, a 3-dB beamwidth of 105 degrees across the stated frequency band and an array gain against isotropic noise of 6 dB can be achieved. We will also show that the four time series vector sensor outputs (x, y and z accelerations and 1 pressure) can be processed to produce multiple nulls in the spatial response or beam pattern, which can be used to null out interfering noise sources. Experimental results, recorded at the US Navy Seneca Lake Test Facility, Dresden, NY using two different designs of the aforementioned 3D vector sensor, show how vector sensors perform in the presence of multiple low-frequency sources and interferers.

This paper considers the problem of tracking an acoustic sources in three dimensional (3-D) space by using a vertical acoustic vector sensor (AVS) array in a shallow ocean environment. The innovations of this work are double fold: 1) a particle filtering (PF) approach is developed to track the position of an acoustic source; and 2) based on the source motion and wave propagation models, the posterior Cramér-Rao bound (PCRB) is derived to provide a lower performance bound of 3-D position tracking in shallow ocean. The PF approach uses a number of samples to approximate the posterior distribution of interested parameters, by which a complex 3-D search can be avoided for 3-D position estimation. Also, due to incorporating both the source dynamic and measurement information, the tracking approach is able to provide a lower performance bound than the traditional localization approach. The tracking performance is further demonstrated by numerical experiments.