Acoustic Vector Sensor Research Articles

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.

Multiple sound sources localization in free field using acoustic vector sensor.

Method and preliminary results of multiple sound sources localization in free field using the acoustic vector sensor were presented in this study. Direction of arrival (DOA) for considered source was determined based on sound intensity method supported by Fourier analysis. Obtained spectrum components for considered signal allowed to determine the DOA value for the particular frequency independently. The accuracy of the developed and practically implemented algorithm was evaluated on the basis of laboratory tests. Both synthetic acoustic signals (pure tones and noises) and real sounds were used during the measurements. Real signals had the same or different energy distribution both on time and frequency domain. The setup of the experiment and obtained results were described in details in the text. Taking the obtained results into consideration is important to emphasize that the localization of the multiple sound sources using single acoustic vector sensor is possible. The localization accuracy was the best for signals which spectral energy distribution was different.

Azimuth-elevation direction finding using a microphone and three orthogonal velocity sensors as a non-collocated subarray

An acoustic vector-sensor consists of three identical but orthogonally oriented acoustic particle-velocity sensors, plus a pressure sensor-all spatially collocated in a point-like geometry. At any point in space, this tri-axial acoustic vector-sensor can sample an acoustic wavefield as a 3 × 1 vector, instead of simply as a scalar of pressure. This vector, after proper self-normalization, would indicate the incident wave-field's propagation direction, and thus the incident emitter's azimuth-elevation direction-of-arrival. This "self-normalization" direction-of-arrival estimator is predicated on the spatial-collocation among the three particle-velocity sensors and the pressure-sensor. This collocation constriction is relaxed here by this presently proposed idea, to realize a spatially distributed acoustic vector-sensor, allowing its four component-sensors to be separately located. This proposed scheme not only retains the algorithmic advantages of the aforementioned "self-normalization" direction-of-arrival estimator, but also will significantly extend the spatial aperture to improve the direction-finding accuracy by orders of magnitude.

Three-Dimensional Localization of a Near-Field Emitter of Unknown Spectrum, using an Acoustic Vector Sensor Corrupted by Additive Noise of Unknown Spectrum

Algorithms are developed to estimate a near-field wideband emitter's azimuth-elevation direction of arrival plus radial distance, based on data collected by one acoustic vector sensor. These new algorithms need no prior knowledge of the spectra of the incident source nor of the temporally colored noise.

Time-space transform: A novel signal processing approach for an acoustic vector-sensor

A new signal processing method using a single vector hydrophone is proposed for solving the problem of azimuth angle estimation for multiple targets based on a small aperture underwater platform. The method extends the aperture from a single vector hydrophone into a half wavelength distance uniform linear array by decomposing the time-domain sample data from different channels of the hydrophone. The extended array has a narrower space beam pattern than a single vector hydrophone. The azimuths of spatial multiple targets are estimated by using the appended array snapshots under the condition of broadband or narrow band signals. The new method is robust because there is no need to correct the array manifold. Theoretical analysis and computer simulations show that, the new algorithm has the ability to distinguish two incoherent targets with either narrow band or broadband signals in an isotropic noise field. The algorithm provides a non-biased estimate with a high signal-to-noise ratio.

Two-Dimensional DOA Estimation of Coherent Sources for Acoustic Vector-Sensor Array Using a Single Snapshot

This paper discusses the problem of two-dimensional direction of arrival estimation of coherent sources for acoustic vector sensors array. Compared with subspace-based methods, such as root multiple signal classification and estimation of signal parameters via rotational invariance technique, the propagator method (PM) has lower computational complexity. However, only in high-snapshots situation, can the PM algorithm enjoy a better estimation performance. Besides, all of these algorithms mentioned above cannot work for coherent sources. In this paper, we combine PM algorithm with Toeplitz Hermitian matrix reconstruction, and propose an improved algorithm, which works well in the case of coherent signals and a single snapshot. Furthermore, the proposed method can achieve automatically paired two-dimensional angle estimation. Simulation results verify that the proposed method has the better angle performance and less computational complexity than spatial smoothing methods.

A high resolution robust localization approach of high speed target based on vector sonar

Based on high speed moving target robust high-resolution direction of arrival (DOA) estimation problem under low signal-to-noise ratio and a small number of snapshots in the underwater acoustic vector signal processing framework, a novel spatial spectrum model combined with compressive sensing method is proposed. By studying the acoustic vector sensor structure, a generalized temporal filtering method based on sound pressure and particle velocity combined treatment is presented. According to the matrix spatial prefiltering theory, a new spatial filter with stopband constraint and mean square error min-max principle in passband is proposed which is used as vector sonar spatial prefiltering algorithm. Based on the methods above, a novel time-space domain jointly filtering high-resolution DOA estimation algorithm based on compressive sensing is proposed. The mathematical model, physical interpretation, and specific implement are explained in detail. Theoretical analysis and computer simulation results show that the new method has a lower dual-target distinguishing threshold and a higher estimation accuracy in solving the vector sonar high speed moving target robust DOA estimation problem under a small number of snapshots (single snapshot) condition. The higher robustness and better results of the proposed method are verified in the lake experiment.

Direction-of-arrival estimation for a uniform circular acoustic vector-sensor array mounted around a cylindrical baffle

This work investigates the direction-of-arrival (DOA) estimation for a uniform circular acoustic Vector-Sensor Array (UCAVSA) mounted around a cylindrical baffle. The total pressure field and the total particle velocity field near the surface of the cylindrical baffle are analyzed theoretically by applying the method of spatial Fourier transform. Then the so-called modal vector-sensor array signal processing algorithm, which is based on the decomposed wavefield representations, for the UCAVSA mounted around the cylindrical baffle is proposed. Simulation and experimental results show that the UCAVSA mounted around the cylindrical baffle has distinct advantages over the same manifold of traditional uniform circular pressure-sensor array (UCPSA). It is pointed out that the acoustic Vector-Sensor (AVS) could be used under the condition of the cylindrical baffle and that the UCAVSA mounted around the cylindrical baffle could also combine the anti-noise performance of the AVS with spatial resolution performance of array system by means of modal vector-sensor array signal processing algorithms.

Self-adapting root-MUSIC algorithm and its real-valued formulation for acoustic vector sensor array

Abstract 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.

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.

Recursive Least-Squares Source Tracking using One Acoustic Vector Sensor

An acoustic vector-sensor (a.k.a. vector-hydrophone) is composed of three acoustic velocity-sensors, plus a collocated pressure-sensor, all collocated in space. The velocity-sensors are identical, but orthogonally oriented, each measuring a different Cartesian component of the three-dimensional particle-velocity field. This acoustic vector-sensor offers an azimuth-elevation response that is invariant with respect to the source's center frequency or bandwidth. This acoustic vector-sensor is adopted here for recursive least-squares (RLS) adaptation, to track a single mobile source, in the absence of any multipath fading and any directional interference. A formula is derived to preset the RLS forgetting factor, based on the prior knowledge of only the incident signal power, the incident source's spatial random walk variance, and the additive noise power. The work presented here further advances a multiple-forgetting-factor (MFF) version of the RLS adaptive tracking algorithm, that requires no prior knowledge of these aforementioned source statistics or noise statistics. Monte Carlo simulations demonstrate the tracking performance and computational load of the proposed algorithms.

Nonlinear Inversion of Acoustic Scalar and Vector Field Transfer Functions

A study to investigate the use of the acoustic vector field, separately or in combination with the scalar field, to invert for geoacoustic properties of the seafloor was conducted. The analysis was performed in the context of the 2004 Sediment Acoustics Experiment (SAX04) conducted in the Northern Gulf of Mexico (GOM) where a small number of acoustic vector sensors were deployed in close proximity to the seafloor. The acoustic vector sensors were located both above and beneath the seafloor interface where they measured the acoustic pressure and the acoustic particle acceleration. A variety of acoustic waveforms were transmitted into the seafloor at normal incidence. Motion data provided by the buried vector sensors were affected by a suspension response that was sensitive to the mass properties of the sensor, the sediment density, and shear wave speed. The suspension response for the buried vector sensors included a resonance within the analysis band of 0.4-2.0 kHz. The response was sufficiently sensitive to the local geoacoustic properties, that it was integrated into the inverse methods developed for this study. Inversions of real and synthetic data sets showed that information about sediment shear wave speed was carried by the suspension response of the buried sensors, as opposed to being contained inherently within the vector acoustic field.

A Lower Bound of Direction-of-Arrival Estimation for an Acoustic Vector Sensor Subject to Sensor Breakdown

The work presented here derives an approximate lower bound (ALB) for the error variance of direction-finding using a single acoustic vector sensor subject to random breakdown in its sensors. This ALB equals a weighted sum of Cramer-Rao bounds (CRBs), conditioned on disjoint events of sensor breakdown, that together define the overall phenomenon of random sensor breakdown. Its tightness is verified by Monte Carlo simulation of a maximum likelihood estimator.

Particle Filtering Approaches for Multiple Acoustic Source Detection and 2-D Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

This paper considers the problem of tracking multiple acoustic sources using a single acoustic vector sensor (AVS). Firstly, a particle filtering (PF) approach is developed to track the direction of arrivals of fixed and known number of sources. Secondly, a more realistic tracking scenario which assumes that the number of acoustic sources is unknown and time-varying is considered. A random finite set (RFS) framework is employed to characterize the randomness of the state process, i.e., the dynamics of source motion and the number of active sources, as well as the measurement process. As deriving a closed-form solution for the multi-source probability density is difficult, a particle filtering approach is employed to arrive at a computationally tractable approximation of the RFS densities. The proposed RFS-PF algorithm is able to simultaneously detect and track multiple sources. Simulations under different tracking scenarios demonstrate the ability of the proposed approaches in tracking multiple acoustic sources.

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be employed in surveillance systems to monitor behavior of public events participants. The results can be used to detect sound source position in real time or to calculate the spatial distribution of sound energy in the environment. Moreover, the spatial filtration can be performed to separate sounds arriving from a chosen direction.

Influences on the spatial directivity of acoustic vector sensor by finite cylinder baffle and future prospects about eliminating the effects

The directivity of acoustic vector sensor can be distorted by the sound diffraction wave of baffle. According to Helmholtz integral equation, the directivity of acoustic vector sensor under the condition of finite cylinder baffle is calculated by using boundary element method (BEM). Considering the problem of nearly singular integrals of BEM, the exponent parts of fundamental solutions are expanded in trigonometric functions. The singular and the nonsingular parts are separated: the nonsingular parts are calculated by Gaussian integral method; the singular parts are regularized by subsection integral method. Then the surface integrals are reduced into line integrals along the elements’ contour which can be calculated by Gaussian integral method. The sound diffraction field of a plane wave under the condition of finite cylinder baffle at different frequencies and incident angles is calculated, and the characteristics of directivity of pressure and vibration velocity at different frequencies are analyzed. The experimental data are treated and the errors between the experimental and theoretical results are analyzed. Finally, according to the research results about the influences on the directivity of acoustic vector sensor by baffle at present, some future prospects about eliminating the effects of sound diffraction field by baffle are presented.

Near fields scattered by an underwater finite cylindrical baffle

In this work, acoustic vector characteristics of near fields scattered by an underwater finite cylindrical baffle are investigated theoretically and experimentally. The analytic expressions for the scattered pressure and particle velocity are derived using the elastic thin shell theory. Calculations are presented for the scattered near fields of the pressure, the particle velocity and the intensity. It is found that the pressure and the particle velocity fields near the surface of the cylindrical baffle are characterized by complex interference structure, particle velocity directions and the source bearings are not consistent. The phase difference between the pressure and the particle velocity is not zero and the intensity vector does not reflect the sound bearings. It can be noted that the distortions of the fields will make the original vector signal processing method based on the free space assumption be no longer applicable in the presence of the cylindrical baffle. These results can serve as a basis of the application for the acoustic vector sensor on board.

Fiber-optic, cantilever-type acoustic motion velocity hydrophone

The interaction between fluid loaded fiber-optic cantilevers and a low frequency acoustic wave is investigated as the basis for an acoustic vector sensor. The displacements of the prototype cantilevers are measured with an integrated fiber laser strain sensor. A theoretical model predicting the frequency dependent shape of acoustically driven planar and cylindrical fiber-optic cantilevers incorporating effects of fluid viscosity is presented. The model demonstrates good agreement with the measured response of two prototype cantilevers, characterized with a vibrating water column, in the regime of Re ≥ 1. The performance of each cantilever geometry is also analyzed. Factors affecting the sensor performance such as fluid viscosity, laser mode profile, and support motion are considered. The planar cantilever is shown to experience the largest acoustically induced force and hence the highest acoustic responsivity. However, the cylindrical cantilever exhibits the smoothest response in water, due to the influence of viscous fluid damping, and is capable of two axis particle velocity measurement. These cantilevers are shown to be capable of achieving acoustic resolutions approaching the lowest sea-state ocean noise.

Particle Filtering and Posterior Cramér-Rao Bound for 2-D Direction of Arrival Tracking Using an Acoustic Vector Sensor

Acoustic vector sensor (AVS) measures acoustic pressure as well as particle velocity, and therefore AVS signal contains 2-D (azimuth and elevation) DOA information of an acoustic source. Existing DOA estimation techniques assume that the source is static and extensively rely on the localization methods. In this paper, a particle filtering (PF) tracking approach is developed to estimate the 2-D DOA from signals collected by an AVS. A constant velocity model is employed to model the source dynamics and the likelihood function is derived based on a maximum likelihood estimation of the source amplitude and the noise variance. The posterior Cramér-Rao bound (PCRB) is also derived to provide a lower performance bound for AVS signal based tracking problem. Since PCRB incorporates the information from the source dynamics and measurement models, it is usually lower than traditional Cramér-Rao bound which only employs measurement model information. Experiments show that the proposed PF tracking algorithm significantly outperforms Capon beamforming based localization method and is much closer to the PCRB even in a challenging environment (e.g., SNR = -10 dB).

Maximum likelihood estimation of direction of arrival using an acoustic vector-sensor

A vector-sensor consisting of a monopole sensor collocated with orthogonally oriented dipole sensors is used for direction of arrival (DOA) estimation in the presence of an isotropic noise-field or internal device noise. A maximum likelihood (ML) DOA estimator is derived and subsequently shown to be a special case of DOA estimation by means of a search for the direction of maximum steered response power (SRP). The problem of SRP maximization with respect to a vector-sensor can be solved with a computationally inexpensive algorithm. The ML estimator achieves asymptotic efficiency and thus outperforms existing estimators with respect to the mean square angular error (MSAE) measure. The beampattern associated with the ML estimator is shown to be identical to that used by the minimum power distortionless response beamformer for the purpose of signal enhancement.