Single Acoustic Vector Sensor Research Articles

Single acoustic vector sensor DOA enhanced by unsaturated bistable stochastic resonance with linear amplitude response constrained

Acoustic vector sensors (AVS) are increasingly utilized for remote target passive perception, but accurate direction-of-arrival (DOA) estimation remains a formidable challenge, particularly in the presence of heavy background noise. In this paper, we propose a novel DOA estimation method that utilizes complex acoustic intensity measurements (CAIM) and unsaturated bistable stochastic resonance with linear amplitude response constrained (LAR-UBSR). The proposed method aims to exploit the excellent denoising performance of stochastic resonance (SR) to enhance the accuracy of DOA estimation. The proposed LAR-UBSR method is based on the theoretical analysis of the signal pre-processing requirements of CAIM, which determines the linear amplitude response and fixed phase shift. LAR-UBSR achieves a stable phase shift and linear amplitude response by utilizing the unsaturated potential well and limited potential parameters adjustment method of SR. We analyze the amplitude response linearity of LAR-UBSR under different signal-to-noise ratios (SNRs) and a wide range of frequencies, demonstrating a larger linear amplitude response interval that meets the signal pre-processing requirements of CAIM. Numerical analysis and application verification show that the proposed DOA method outperforms existing methods, particularly under low SNR conditions. The results suggest the potential for high-accuracy DOA estimation using nonlinear signal processing strategies in the presence of heavy background noise.

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering.

Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time-frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.

Direction Estimation and Tracking of Coherent Sources Using a Single Acoustic Vector Sensor

A single acoustic vector sensor (AVS) cannot be used to find the direction-of-arrival (DOA) of two or more coherent (fully correlated) sources. We have proposed a technique for estimating DOAs (in 2D geometry) of two simultaneous coherent sources using single AVS under the assumption that acoustic sources enter in the field sequentially. The DOA estimation has been investigated with two different configurations of AVS, each consisting of three microphones in a plane. The technique has been also applied in tracking (a) an acoustic source in the presence of stationary interfering coherent source and (b) two coherent sources when the sources are changing their locations alternatively. The experimental environment has been generated using the Finite-Element Method tool viz. COMSOL to corroborate the proposed scheme.

High-resolution DOA estimation achieved by a single acoustic vector sensor under anisotropic noise

The acoustic vector sensor (AVS) has a wide application in the direction of arrival (DOA) estimation of sound sources including aircraft, underwater or on-land vehicles, search-and-rescue efforts, etc. The performance of only one acoustic vector sensor can compare favorably with a traditional acoustic array in DOA estimation. The anisotropic noise problem of the vector sensor has had a severe affective on the estimation accuracy and has caused little attention until now. In this paper, an AVS measurement system is designed, and an Anisotropic Sparse Bayesian Learning (SBL) method (the A-SBL method) is proposed to model and estimate the anisotropic noise of the AVS system. A high-resolution DOA estimation method is also introduced based on the noise estimation algorithm. The experimental results exhibit the necessity of modeling the anisotropic noise, and the estimation accuracy of the proposed A-SBL method based on the noise model is verified. The comparison of DOA methods shows the superiority of the proposed method, in which the DOA estimation error is not more than 1°. The proposed method’s utility may be found in applications for localization or tracking of targets with prominent acoustic characteristics when the environment is complicated.

Non-cooperative Underwater Acoustic Frequency Hopping Signal Receiving System and Azimuth Estimation Method Based on Single Acoustic Vector Sensor

Non-cooperative Underwater Acoustic Frequency Hopping Signal Receiving System and Azimuth Estimation Method Based on Single Acoustic Vector Sensor

Source separation and arrival angle estimation using a single acoustic vector sensor

Extracting individual time signatures from two angularly-separated sources overlapping in time and bandwidth typically requires coherent array processing on multiple hydrophones, a process whose performance is degraded by limited aperture and spatial aliasing. Vector sensors, which measure both acoustic pressure and particle velocity, can measure an acoustic field’s directionality in a single, compact sensor package. Standard beamforming techniques can be applied to the outputs of a vector sensor to suppress one source’s signal, but its efficacy is limited by the angular insensitivity of the resulting cardioid beam pattern. Here we use fundamental physical principles to demonstrate that measurements on a 2-D vector sensor can completely determine the azimuths, amplitudes, and relative phases of two horizontal plane waves of the same frequency, achieving source separation and direction-of-arrival estimates for both. Interference between the waves creates a reactive intensity whose orientation is perpendicular to the bisector of the two wavenumber vectors. This observation leads to closed-form inversion formulas. Data examples of the technique are illustrated using overlapping humpback whale songs off Hawaii, and on weak airgun and bowhead whale signals embedded within intense directional noise from a nearby drilling site in the Beaufort Sea. [Work sponsored by ONR TFO.]

Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion.

The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.

A Time-Frequency Bins Selection Pipeline for Direction-of-Arrival Estimation Using a Single Acoustic Vector Sensor

The case that multiple speakers are simultaneously active is usually regarded as adverse condition in the context of multi-source direction-of-arrival (DOA) estimation. Moreover, in adjacent sources scenarios, classifying DOA estimates by directions may merge adjacent sources and misclassify reverberation as sources, i.e., there is a mismatch between sources and reverberation. In order to address the open challenges mentioned above, this work introduces a pipeline for selecting reliable time-frequency (TF) bins and estimating multi-source DOAs using a single acoustic vector sensor (AVS). In the proposed pipeline, we show a viewpoint that the multiple simultaneously active sources condition can be exploited to select TF bins solely dominated by sources. The consistency of the distribution of intensity vectors (IVs) in multi-source scenarios is derived mathematically to support the proposed viewpoint. We address the mismatch between sources and reverberations in adjacent sources scenarios by focusing only on the direction with the highest intensity vector density (IVD), regardless of how many sources dominate the given TF window. Simulation and experimental results show that the proposed method can accurately and robustly estimate the multi-source DOAs in various scenarios.

2qth-Order cumulants based virtual array of a single acoustic vector sensor

A spatially co-located four-element single acoustic vector sensor offers numerous advantages over the scalar pressure sensor array such as compact size, ability to handle spatially undersampled systems, and the intrinsic two-dimensional directivity independent of the range, frequency, and bandwidth of the incoming signal. Despite all these advantages, a single acoustic vector sensor finds limited application in the field of the direction of arrival estimation as it can only resolve a maximum of two sound sources using classical second-order statistics-based methods. This paper proposes a 2qth-order circular cumulants based processing (q is an integer, and q≥1) by utilizing the concept of virtual array, to enhance the degree of freedom and hence to maximize the sources resolvability of a single acoustic vector sensor. Additionally, this paper derives a theoretical upper bound on the maximum number of the identifiable two-dimensional direction of arrivals of statistically independent, non-Gaussian sources by exploiting the proposed virtual array of a single acoustic vector sensor. The upper bound on the maximum number of the identifiable two-dimensional direction of arrivals by exploiting the proposed virtual array is found to be 3, 8, and 14, for q=1, 2, and 3, respectively, if both the elevations and azimuths are distinct for all the given statistically independent, non-Gaussian sources. Furthermore, the upper bound on the maximum number of the identifiable two-dimensional direction of arrivals by exploiting the proposed virtual array is found to be 2, 4, and 6, for q=1, 2, and 3, respectively, if all the given statistically independent, non-Gaussian sources are lying in a single azimuth (or elevation) plane with distinct elevations (or azimuths).

Multi-Level Time-Frequency Bins Selection for Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

In the context of multi-source direction of arrival (DOA) estimation in an enclosed environment, the challenges include reverberation and overlapping of multiple simultaneous active sources. To address these interferences, the identification of time-frequency (TF) bins dominated by the sources signals is essential. In this work, we propose an intensity vector (IV) based TF bins selection technique for DOA estimation using a single acoustic vector sensor (AVS). The proposed technique involves multi-level inliers selection and outliers removal (MLISOR), which is implemented in three steps. In the first step, we derive the distribution of IVs and then select IVs using a norm metric. In the second step, the regions with the highest local IV density in each time frame are identified. In the third step, we cluster the IVs according to their directions and remove the outliers based on the member-to-centroid angle metric. Simulation results show that both the accuracy and the robustness of the proposed technique outperform the existing techniques. The indoor experimental results also verify that the proposed technique is effective and robust in practical situations.

Recurrent networks for direction-of-arrival identification of an acoustic source in a shallow water channel using a vector sensor.

Conventional direction-of-arrival (DOA) estimation algorithms for shallow water environments usually contain high amounts of error due to the presence of many acoustic reflective surfaces and scattering fields. Utilizing data from a single acoustic vector sensor, the magnitude and DOA of an acoustic signature can be estimated; as such, DOA algorithms are used to reduce the error in these estimations. Three experiments were conducted using a moving boat as an acoustic target in a waterway in Houghton, Michigan. The shallow and narrow waterway is a complex and non-linear environment for DOA estimation. This paper compares minimizing DOA errors using conventional and machine learning algorithms. The conventional algorithm uses frequency-masking averaging, and the machine learning algorithms incorporate two recurrent neural network architectures, one shallow and one deep network. Results show that the deep neural network models the shallow water environment better than the shallow neural network, and both networks are superior in performance to the frequency-masking average method.

DOA Estimation via Acoustic Sensor Array With Non-Orthogonal Elements

A single two-dimensional acoustic vector sensor (AVS) has been widely used to estimate the direction of arrival (DOA) of an acoustic signal. In general, the existing techniques are derived under the assumption that the two velocity sensors are orthogonal. However, the velocity sensors may not be perfectly orthogonal and the angle mismatch will cause the DOA estimation performance to deteriorate. In this paper, we propose an iterative DOA estimation method to solve this angle deviation issue. In particular, we first introduce the deviation parameter into the DOA estimation problem, and quantify the DOA estimation bias based on the two non-orthogonal AVS models. It is shown that the non-orthogonal sensor elements result in an asymptotically biased DOA estimate. Then, a new iterative method is proposed to estimate jointly the DOA of acoustic source and the non-orthogonal deviation matrix (or angle deviation). It is shown that the proposed method can be applied in conjunction with conventional DOA estimation methods to improve their estimation accuracy under non-orthogonal AVS. The effectiveness of the proposed method and its extension is confirmed by Monte Carlo simulation as well as underwater experiment.

High-Resolution DOA Estimation Algorithm for a Single Acoustic Vector Sensor at Low SNR

In order to improve the angular resolution of direction-of-arrival (DOA) estimation based on portable microphone at low signal-noise-ratio (SNR), this paper proposes a new high-resolution DOA estimation algorithm based on single acoustic vector sensor (AVS), namely Virtual-AVS algorithm. The innovative algorithm is mainly composed of two parts: the preprocessing method and the DOA estimation method with ambiguity elimination. The preprocessing method is implemented based on higher-order cumulants with Gaussian noise suppression characteristic. This method not only suppress Gaussian noise to effectively enhance SNR, but also achieve AVS virtual expansion. The computational burden can be significantly reduced by effectively expanding one necessary AVS to perform the ESPRIT algorithm. To extend the application range of the innovative algorithm to support non-point-like geometry AVS, we introduce a new DOA estimation method with ambiguity elimination. This method can eliminate the cyclic ambiguity caused by the channel spacing of the non-point-like geometry AVS exceeding the half-wavelength of the incident source. We prove that the Virtual-AVS algorithm is asymptotically unbiased with large number of snapshots. We also derive the Cram $\mathbf {\acute{e}}$ r-Rao bound. The simulations of closely-spaced sound sources show that the algorithm has better angular resolution than the existing algorithms. The real-world experiments of semi-anechoic chamber also verify the feasibility of the algorithm.

Higher-Order-Statistics-Based Direction-of-Arrival Estimation of Multiple Wideband Sources With Single Acoustic Vector Sensor

Acoustic vector sensor (AVS) measures 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 the received signal. This article proposes higher order statistics (HOS)-based DOA estimation with a single acoustic vector sensor in the underdetermined case where the number of sources is more than the number of sensors constituting a single vector sensor. The use of HOS allows the increase in the number of degrees of freedom, thereby resulting in increase in uniquely identifiable sources. It has been established that in an ideal condition, 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 further increased by working with HOS. Additional advantages of the proposed algorithm, such as robustness against the presence of nonidentical Gaussian noise or spatially correlated Gaussian noise at each constituent sensor of a single AVS, improved performance over second-order statistics-based algorithm, and ability to identify multiple wideband sources, have also been demonstrated.

Multisource DOA Estimation in a Reverberant Environment Using a Single Acoustic Vector Sensor

We address the problem of direction-of-arrival DOA estimation for multiple speech sources in an enclosed environment using a single acoustic vector sensor. The challenges in such scenario include reverberation and overlapping of the source signals. In this work, we exploit low-reverberant-single-source LRSS points in the time–frequency TF domain, where a particular source is dominant with high signal-to-reverberation ratio. Unlike conventional algorithms having limitation that such potential points need to be detected at “TF-zone” level, the proposed algorithm performs LRSS detection at “TF-point” level. Therefore, for the proposed algorithm, the potential LRSS points need not be neighbors of each other within a TF zone to be detected, resulting an increased number of detected LRSS points. The detected LRSS points are further screened by an outlier removal step such that only reliable LRSS points will be used for DOA estimation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm in multisource reverberant environments.

Frequency warping transform of the vertical energy flux and its use for passive source ranging

The frequency warping transform of the vertical energy flux and a passive impulsive source ranging method with a single acoustic vector sensor are presented in this paper. The cross-correlation component of two different modes in the vertical energy flux is transformed into separable impulsive sequence. With a guide source, the source range is extracted from the relative delay time of the impulsive sequence. Comparing with warping the pressure autocorrelation function, there is no need to delete the modal autocorrelation component for warping the vertical energy flux, especially for its real part. Besides, the source ranging result based on the time delay of the warped vertical energy flux is much better for closer range. The frequency warping transform of the vertical energy flux and the passive source ranging method are verified by experimental data.

Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor

Enhancing speech captured by distant microphones is a challenging task. In this study, we investigate the multichannel signal properties of the single acoustic vector sensor (AVS) to obtain the inter-sensor data ratio (ISDR) model in the time-frequency (TF) domain. Then, the monotone functions describing the relationship between the ISDRs and the direction of arrival (DOA) of the target speaker are derived. For the target speech enhancement (SE) task, the DOA of the target speaker is given, and the ISDRs are calculated. Hence, the TF components dominated by the target speech are extracted with high probability using the established monotone functions, and then, a nonlinear soft mask of the target speech is generated. As a result, a masking-based speech enhancement method is developed, which is termed the AVS-SMASK method. Extensive experiments with simulated data and recorded data have been carried out to validate the effectiveness of our proposed AVS-SMASK method in terms of suppressing spatial speech interferences and reducing the adverse impact of the additive background noise while maintaining less speech distortion. Moreover, our AVS-SMASK method is computationally inexpensive, and the AVS is of a small physical size. These merits are favorable to many applications, such as robot auditory systems.

Masked beamforming in frequency domain for hybrid acoustic vector sensor array

A masked beamforming algorithm for a hybrid acoustic vector sensor array (AVSA) is presented. In the general literature, a full-rank AVSA, comprised entirely of an acoustic vector sensor (AVS), is known to be capable of left–right ambiguity elimination. However, a full-rank AVSA has disadvantages including rotation perturbation of sensor directivity, complexity of electrical loading, and high cost. To alleviate these disadvantages, a hybrid AVSA consisting of only a single AVS in an array of omni-directional sensors is reported in the patent with a limited performance in left–right ambiguity elimination. A masked beamforming algorithm is proposed to achieve good left–right ambiguity elimination performance in a hybrid AVSA by masking the beam output spectrum of the omni-directional sensor array with the beam output spectrum of an AVS in the frequency domain. Simulation results that validate the performance of the proposed method are also presented.

Acoustic direction finding using single acoustic vector sensor under high reverberation

We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.

Open-Lake Experimental Investigation of Azimuth Angle Estimation Using a Single Acoustic Vector Sensor

Five well-known azimuth angle estimation methods using a single acoustic vector sensor (AVS) are investigated in open-lake experiments. A single AVS can measure both the acoustic pressure and acoustic particle velocity at a signal point in space and output multichannel signals. The azimuth angle of one source can be estimated by using a single AVS in a passive sonar system. Open-lake experiments are carried out to evaluate how these different techniques perform in estimating azimuth angle of a source. The AVS that was applied in these open-lake experiments is a two-dimensional accelerometer structure sensor. It consists of two identical uniaxial velocity sensors in orthogonal orientations, plus a pressure sensor—all in spatial collocation. These experimental results indicate that all these methods can effectively realize the azimuth angle estimation using only one AVS. The results presented in this paper reveal that AVS can be applied in a wider range of application in distributed underwater acoustic systems for passive detection, localization, classification, and so on.