Acoustic vector-sensor beamforming and Capon direction estimation

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.

Open lake experiment for direction of arrival estimation using acoustic vector sensor array

This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method.

A vector sensor measures the scalar and vector components of the acoustic field. In a multipath channel, the sensor collects the information via multiple paths. Depending on the angle-of-arrival distribution and other channel characteristics, different types of correlation appear in a vector sensor array, which affect the array performance. In this paper, a novel statistical framework is developed which provides closed-form parametric expressions for signal correlations in vector sensor arrays. Such correlation expressions serve as useful tools for system design and performance analysis of vector sensor signal processing algorithms. They can also be used to estimate some important physical parameters of the channel such as angle spreads, mean angle of arrivals, etc.

Acoustic emissions from underwater sources are used to detect, locate, and track targets by passive surveillance sensors. The “vector” sensors measure the scalar pressure as well as the orthogonal components of particle velocity and offer distinct advantages over the traditional solution of array of hydrophones due to their smaller size and frequency independent performance over the frequency range specified for constituent elements. This paper presents studies on an in-house implemented prototype vector sensor employing tri-axial accelerometer as constituent element. The tests undertaken include Direction of Arrival (DoA) estimation and application of modified Fast Orthogonal Search technique on a single Vector Sensor for improvement in spectral resolution. We also propose a configuration involving just two Vector Sensors (composed solely of accelerometers) that offers ease of design and obviates the requirement of a pressure sensor in setup for obtaining DoA estimate while also providing range to the source.

We propose a new class of nested vector-sensor arrays, via tensor modeling, which is capable of increasing the number of degrees of freedom (DOFs) for direction-of-arrival (DOA) estimation, compared with the uniform linear array (ULA). By using one component's information of the interspectral tensor, 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> ) DOFs with only N physical sensors. To utilize the increased DOFs, a novel spatial smoothing approach is proposed, which preserves the data structure and avoids reorganization via tensor modeling. Further, we propose corresponding direction of arrival (DOA) estimation strategies. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. We show that the DOA estimation performance is greatly improved due to the combination of the nested-array strategy and tensor modeling. The effectiveness of the proposed methods is verified through numerical examples.

Monopulse based DOA and polarization estimation with polarization sensitive arrays

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.

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.

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.

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.

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.

To mitigate the reduced accuracy of direction-of-arrival (DOA) estimation in scenarios with low signal-to-noise ratios (SNR) and multiple interfering sources, this paper proposes an Auxiliary Classifier Generative Adversarial Network (ACGAN) architecture that integrates a Squeeze-and-Excitation (SE) attention mechanism and a Multi-scale Dilated Feature Aggregation (MDFA) module. In this neural network, a vector hydrophone array is employed as the receiving unit, capable of simultaneously sensing particle velocity signals in three directions and acoustic pressure , thereby providing high directional sensitivity and maintaining robust classification performance under low-SNR conditions. The MDFA module extracts features from multiple receptive fields, effectively capturing cross-scale patterns and enhancing the representation of weak targets in beamforming maps. This helps mitigate estimation bias caused by mutual interference among multiple targets in low-SNR environments. Furthermore, an auxiliary classification branch is incorporated into the discriminator to jointly optimize generation and classification tasks, enabling the model to more effectively identify and separate multiple types of labeled sources. Experimental results indicate that the proposed network is effective and shows improved performance across diverse scenarios.

Acoustic Vector Sensors (AVS) measure acoustic pressure as well as the acoustic particle velocity to estimate the acoustic intensity. The acoustic intensity, which is a vector quantity, represents the magnitude and direction of the active or propagating part of an acoustic field thus indicating the Direction of Arrival (DOA) of a received signal. This paper concerns with the feasibility of DOA estimation with a single acoustic vector sensor in the under-determined case where the number of sources are more than the number of sensors. It is proposed to use higher order statistics (cumulants) to increase the number of uniquely identifiable sources using a single vector sensor. The use of higher order statistics allows the increase in the dimension of the array steering matrix thereby resulting in increase of uniquely identifiable sources. The details of the proposed algorithm along with the number of sources which can be uniquely indentified are presented in this paper. It has been observed that a single AVS using fourth order statistics is capable of uniquely identifying eight sources with not more than four sources in a single plane. The number of sources can be increased by working with further higher order statistics of the order of 2q (for q ≥ 2). All the results of the proposed algorithm have been verified through suitable simulations.

Acoustic data collected from a 22 m long horizontal line array with elements comprised of omni-directional hydrophone sensors co-located with acceleration vector sensor triplets are analyzed for seabed geo-acoustic information. Broadband and continuous wave tone data were recorded from the passage of a surface vessel source, the R/V Montague, in an August 2006 experiment conducted in Monterey Bay. The water depth at the array and along the source track was approximately 85 m. Simultaneous inversions for source track and geo-acoustic parameters were performed with 18 tones from the 62-415 Hz tone data for source ranges 0.5-2.0 km. The efficacy of inversions with vector sensor data is demonstrated by the fact that data from vector sensor acceleration components parallel to the array line of bearing produced inversion solutions approximately identical to the solutions obtained from the inversion of data for the same tones from the hydrophone array components. In addition the source spectra derived from these inversions are in nominal agreement with the source spectra obtained from an independent measurement.