Acoustic Vector Sensor Research Articles

Unambiguous directions of arrival estimation of coherent sources using acoustic vector sensor linear arrays

This work investigates the problem of estimating directions of arrival (DOAs) of coherent sources without the left–right ambiguity by acoustic vector sensor linear arrays. Toeplitz matrices obtained from vector sensor cross covariance matrices are summed to obtain a new matrix with vector sensor directivities as weights. In this way, the proposed method restores the rank of the equivalent signal subspace and eliminates 2π ambiguous DOA information of sources. A robust technique is also developed to improve its performance degradation because of the velocity sensor gain uncertainty during unit conversion, such as imprecise measurements of the sensor sensitivities and the product of the water density and sound speed in the water. The methods can be implemented as a preprocessing stage before applying beamformers, such as multiple signal classification and MVDR. Theoretical analyses show that comparing with existing preprocessing techniques, the proposed methods can fully resolve the coherent sources without the left–right ambiguity while keeping the effective array aperture, and even applicable to the case of closely spaced coherent sources. Their effectiveness and performances are conducted via simulations.

Measurements and Visualization of Sound Intensity Around the Human Head in Free Field Using Acoustic Vector Sensor

This paper presents measurements and visualization of sound intensity around a human head simulator in a free field. A Cartesian robot, applied for precise positioning of the acoustic vector sensor, was used to measure sound intensity. Measurements were performed in a free field using a head and torso simulator and a setup consisting of four different loudspeaker configurations. The acoustic vector sensor was positioned around the head with a 5-cm step. Sound intensity was measured in 277 points. For every step three orthogonal sound intensity components were calculated. Pure tones of frequencies 250, 1000, and 4000 Hz were applied to analyze the acoustic field. Obtained results were used to provide visualizations of sound intensity distribution around the human head. The tool developed for this purpose utilized three-dimensional sound intensity measurements and visualization techniques.

Design of Underwater Acoustic Vector Sensor and its Elastic Suspension Element

Acoustic vector sensor is a kind of inertial sensors. Different from pressure-sensing sensor, it can sense the angle of target under the water. Now most suspension elements are installed on the acoustic vector sensor later. This paper outlines an acoustic vector sensor which is encased in silicon rubber. In this paper, a new structure of acoustic vector sensor is present. The radial stiffness of silicon rubber spring is analyzed by using theory and simulation calculation based on the finite element software ANSYS. At last, the acoustic vector sensor is measured. The results show that the new structural design of acoustic vector sensor could reduce the adverse impact of suspending elastic suspension elements repeatedly and improve the reliability of acoustic vector sensor.

Modeling of Sound-structure Interactions for Designing a Piezoelectric Micro-Cantilever Acoustic Vector Sensor

An acoustic vector sensor is a device that is capable of measuring the direction of wave propagation and the acoustic pressure. In this paper, the modeling of micro-cantilever sensor for the vector sensor are proposed by consideration of acoustic phenomenon in water. Two models based on unimorph structure are proposed in this paper and corresponding transfer function which describes the relation between input pressure wave and output voltage depending on incidence angle and frequency of pressure wave is derived based on lumped model. It has been shown that very thin and flexible micro-cantilever can be used to measure directly the particle velocity component in water.

Experimental Results for Direction of Arrival Estimation with a Single Acoustic Vector Sensor in Shallow Water

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.

Source ranging based on frequency band decomposition and distance weighting using a single acoustic vector sensor in shallow water

A novel method is proposed for the passive source range estimation based on union processing of pressure and particle horizontal velocity. Autocorrelation functions’ warping spectra of pressure and particle velocities have frequency invariability. The spectra of the warped autocorrelation functions of pressure and particle horizontal velocity have the same line spectrum feature, while the spectrum of the warped autocorrelation function of particle vertical velocity possesses both line and broadband spectrum features. Moreover, the warped autocorrelation function’s spectrum of particle vertical velocity has more peaks, and the peak width is broader than those of pressure and particle horizontal velocity. In this paper, source ranges are estimated based on frequency band decomposition and distance weighting, and a guided source with a known range is employed to provide the crucial frequency invariant features. Experimental data in shallow water with an iso-speed profile are used to verify the approach which can reasonably estimate source ranges with the relative errors of the source ranging basically less than 7%. This method can effectively reduce the mainlobe width and background level of the cost function, and can significantly improve the resolution of source range estimation, compared with the results of conventional source ranging approach that uses a single pressure hydrophone.

New research on MEMS acoustic vector sensors used in pipeline ground markers.

According to the demands of current pipeline detection systems, the above-ground marker (AGM) system based on sound detection principle has been a major development trend in pipeline technology. A novel MEMS acoustic vector sensor for AGM systems which has advantages of high sensitivity, high signal-to-noise ratio (SNR), and good low frequency performance has been put forward. Firstly, it is presented that the frequency of the detected sound signal is concentrated in a lower frequency range, and the sound attenuation is relatively low in soil. Secondly, the MEMS acoustic vector sensor structure and basic principles are introduced. Finally, experimental tests are conducted and the results show that in the range of 0°∼90°, when r = 5 m, the proposed MEMS acoustic vector sensor can effectively detect sound signals in soil. The measurement errors of all angles are less than 5°.

Experimental demonstration of differential OFDM underwater acoustic communication with acoustic vector sensor

A new Orthogonal Frequency Division Multiplexing (OFDM) based underwater acoustic communication scheme utilizing acoustic vector sensor is presented. The acoustic channel assumed has the slow time-varying characteristic and the neighboring signals can be considered as transmitted through the same channel. The differential modulation encodes information through the relative phase changes between adjacent subcarriers. Therefore, instead of adding pilots to monitor the channel, the differential demodulation is used to reconstructs transmitted information without necessity of channel estimation. This increases the transmission rate and reduce the complexity of processor at the receiver. An acoustic vector sensor is used in the differential OFDM system to further improve the performance of the system with 4.8dB gains that can be obtained by combined processing pressure and velocity signals from the acoustic vector sensor. To validate the capability of the proposed scheme, an experiment with picture and voice source information was conducted in a lake. The results obtained at different distances demonstrate the proposed system offers a significant advantage.

New Research on MEMS Acoustic Vector Sensor Used in ground Marker of Pipeline

Recently, ground marker based on the principle of acoustic detection has become the trend of pipeline technology. As to the difficulty and low accuracy of ground marker, this paper introduces a new MEMS bionic acoustic vector sensor with high sensitivity, good performance in low frequency and lower power. As to the problem of the port/starboard blur of this vector sensor in ground marking, it proposes a new plan and proves the correctness of the plan through theoretic analysis and experimental verification. The results show that the mean error of the directional angle is within 5 degree meeting the needs of the engineering project.

New Research on MEMS Acoustic Vector Sensor Used in ground Marker of Pipeline

New Research on MEMS Acoustic Vector Sensor Used in ground Marker of Pipeline

Processing of acoustical data in a multimodal bank operating room surveillance system

An automatic surveillance system capable of detecting, classifying and localizing acoustic events in a bank operating room is presented. Algorithms for detection and classification of abnormal acoustic events, such as screams or gunshots are introduced. Two types of detectors are employed to detect impulsive sounds and vocal activity. A Support Vector Machine (SVM) classifier is used to discern between the different classes of acoustic events. The methods for calculating the direction of coming sound employing an acoustic vector sensor are presented. The localization is achieved by calculating the DOA (Direction of Arrival) histogram. The evaluation of the system based on experiments conducted in a real bank operating room is presented. The results of sound event detection, classification and localization are provided and discussed. The practical usability of the engineered methods is underlined by presenting the results of analyzing a staged robbery situation.

A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramér–Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms.

A modified direction-of-arrival estimation algorithm for acoustic vector sensors based on Unitary Root-MUSIC

A novel method applying for direction-of-arrival(DOA) using acoustic vector sensors(AVS) based on Unitary Root-MUSIC algorithm(URM) is proposed in this paper. AVS array has a characteristic named coherence principle of sound pressure and velocity, which can significantly improve the detection performance of DOA by reducing the influence of white Gaussian noise. We apply this characteristic and the extra velocity information of AVS to construct a modified covariance matrix. In particular, the modified covariance matrix need not extend the dimension in calculation of AVS covariance matrix which means saving the computing time. In addition, we combine the characteristics of modified matrix with URM algorithm to design a new algorithm, which can minimize the impact of environment noise and further reduce computational complexity to a lower order of magnitude. So the proposed method can not only improve the accuracy of DOA detection but also reduce the computational complexity, compared to the classic DOA algorithm. Theory analysis and simulation experiment show that the proposed algorithm for AVS based on URM can significantly improve the DOA resolution in low SNR ratios and few snapshots.

Source Number Detectability by an Acoustic Vector Sensor Linear Array and Performance Analysis

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.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

Multiple Wideband Acoustic Source Tracking in 3-D Space Using a Distributed Acoustic Vector Sensor Array

This paper considers the problem of tracking multiple acoustic sources in 3-D space using a distributed acoustic vector sensor array. Unlike the existing two-stage localization approach, which estimates the direction of arrival of the source at each sensor first and then triangulate a 3-D position, a particle filtering approach is developed to directly fuse the signals collected from distributed sensors. To enhance the tracking performance and constrain the computational complexity, an information filter is developed to approximate the optimal importance sampling. Since the position state of the source is linear with the velocity state, a Rao-Blackwellization step is employed to marginalize out the velocity component. In addition, the posterior Cramer-Rao bound is developed to provide a lower performance bound for the distributed tracking system. Both the numerical study and simulations show that the proposed tracking approach significantly outperforms the two-stage localization approaches for 3-D position estimation.

An Hybrid Cramér-Rao Bound in Closed Form for Direction-of-Arrival Estimation by an “Acoustic Vector Sensor” With Gain-Phase Uncertainties

An “acoustic vector sensor” (also known as a “vector hydrophone” in underwater or sea-surface applications) is composed of three orthogonally oriented uni-axial particle-velocity sensors, plus a “pressure-sensor” (i.e., a microphone or a hydrophone) - all collocated in a point-like spatial geometry. (This collocated setup is versatile for direction finding, because its azimuth-elevation spatial response is independent of frequency.) This paper investigates how the acoustic vector sensor's direction finding accuracy would be degraded by random deviations from its nominal gain response and/or phase response. Each type of deviation is statistically modeled herein as a random variable with a small variance, reasonably so for a well-built acoustic vector sensor. The resulting hybrid Cramér-Rao bound (HCRB) is derived exactly in open form for azimuth-elevation arrival-angle estimation, but also approximated to produce a closed form that is simple enough to yield qualitative observations. This closed-form hybrid Cramér-Rao lower bound's tightness is illustrated by a new estimator developed in this paper.

Nested Vector-Sensor Array Processing via Tensor Modeling

We propose a new class of nested vector-sensor arrays which is capable of significantly increasing the degrees of freedom (DOF). This is not a simple extension of the nested scalar-sensor array, but a novel signal model. The structure is obtained by systematically nesting two or more uniform linear arrays with vector sensors. By using one component's information of the interspectral tensor, which is equivalent to the higher-dimensional second-order statistics of the received data, the proposed nested vector-sensor array can provide O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) DOF with only N physical sensors. To utilize the increased DOF, a novel spatial smoothing approach is proposed, which needs multilinear algebra in order to preserve the data structure and avoid reorganization. Thus, the data is stored in a higher-order tensor. Both the signal model of the nested vector-sensor array and the signal processing strategies, which include spatial smoothing, source number detection, and direction of arrival (DOA) estimation, are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. The effectiveness of the proposed methods is verified through numerical examples.

“Quasi-Blind” Calibration of an Array of Acoustic Vector-Sensors That Are Subject to Gain Errors/Mis-Location/Mis-Orientation

This paper advances a new “quasi-blind” calibration algorithm to calibrate a multi-array network (MAN) of acoustic-vector-sensors, whose component-sensors may have non-ideal gain/phase responses, incorrect orientations, and imprecise locations. This proposed calibration is “quasi-blind” in not requiring any prior knowledge/estimation of any training signal's arrival-angle. This proposed algorithm is computationally orders-of-magnitude more efficient than maximum-likelihood estimation. These advantages are achieved here by exploiting the acoustic vector-sensor's quintessential characters, to interplay between two complementary approaches of direction-finding: (1) customary interferometry between vector-sensors, and (2) “acoustic particle-velocity-field normalization” DOA-estimation within each individual vector-sensor. Monte Carlo simulations verify the proposed algorithm's efficacy in “quasi-blind” calibration and its aforementioned computational efficacy.

Speech enhancement with an acoustic vector sensor: an effective adaptive beamforming and post-filtering approach

Speech enhancement has an increasing demand in mobile communications and faces a great challenge in a real ambient noisy environment. This paper develops an effective spatial-frequency domain speech enhancement method with a single acoustic vector sensor (AVS) in conjunction with minimum variance distortionless response (MVDR) spatial filtering and Wiener post-filtering (WPF) techniques. In remote speech applications, the MVDR spatial filtering is effective in suppressing the strong spatial interferences and the Wiener post-filtering is considered as a popular and powerful estimator to further suppress the residual noise if the power spectral density (PSD) of target speech can be estimated properly. With the favorable directional response of the AVS together with the trigonometric relations of the steering vectors, the closed-form estimation of the signal PSDs is derived and the frequency response of the optimal Wiener post-filter is determined accordingly. Extensive computer simulations and a real experiment in an anechoic chamber condition have been carried out to evaluate the performance of the proposed algorithm. Simulation results show that the proposed method offers good ability to suppress the spatial interference while maintaining comparable log spectral deviation and perceptual evaluation of speech quality performance compared with the conventional methods with several objective measures. Moreover, a single AVS solution is particularly attractive for hands-free speech applications due to its compact size.