Optimization of acoustic sensor arrays for wind tunnel measurements

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.

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 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.

Ambient seismic noise beamforming tries to identify the direction (traditional one component beamforming) or the direction and wavetype (three component beamforming) of the incoming ambient noise field. If the signal is weak, either taking longer time intervals or stacking several consecutive time windows is employed to obtain a sharper image of the underlying noise sources. However, this also reduces the temporal resolution of the noise sources and might prevent the identification of distinct sources in the case of a temporally highly variable noise field. In addition, the presence of very local, often station specific, noise sources can compromise the coherence of the seismic noise field, preventing the identification of noise sources. The identification and subsequent removal of either time intervals or single stations with exceptionally low signal correlation could thus also lead to an improvement in the temporal resolution of the seismic noise sources and the classification of the ambient seismic noise field.In this study, we investigate the ambient seismic noise field recorded with different temporary short period seismic networks in the Euregio Meuse-Rhine (EMR). The region is a candidate site for the next generation gravitational wave telescope (Einstein telescope) and characterizing the anthropogenic ambient surface noise field with high spatial and temporal variability containing many different noise sources (e.g. highways, railway lines, industry, wind turbines, urban settlements) is essential. We investigate the spatio-temporal coherence of the seismic wavefield to identify time intervals and regions with high waveform coherence which are then investigated for possible ambient noise sources. The one component and three component (when available) beamforming results are compared with results from matched field processing (MFP) to validate that the sources are outside the recording network and to estimate the possible geographic source area.We observe a clear diurnal character of the coherence of the ambient seismic noise field over a wide frequency range from about 2 to 20 Hz with significantly higher coherence values during night. This indicates the prevalence of very local noise contributions during working hours. In addition, a clear anti-correlation between wavefield coherence and wind speed also indicates the local character of wind generated noise. Limiting beamforming analysis to time windows and stations with high coherence improves the beamforming results and the temporal resolution during time intervals identified as coherent. Beamforming results during time intervals with high waveform coherence often show stable backazimuth directions indicating persistent ambient noise sources. The source location outside the recording network is confirmed by the results of the MFP analysis. This also holds for time intervals during working hours and can be confirmed by MFP processing. Results from such investigations might be used as best practices for the spatio-temporal characterization of ambient noise sources in the case of highly complex noise fields.

The microphone data collected in aeroacoustic wind tunnel test contains not only desired aeroacoustic signal but also background noise generated by the jet or the valve of the wind tunnel, so the desired aeroacoustic characteristics is difficult to be highlighted due to the low Signal-to-Noise Ratio (SNR). Classical cross spectral matrix removal can only reduce the microphone self-noise, but its effect is limited for jet noise. Therefore, an Airflow Background Noise Suppression method based on the Ensemble Empirical Mode Decomposition (ABNSEEMD) is proposed to eliminate the influence of background noise on aeroacoustic field reconstruction. The new method uses EEMD to adaptively separate the background noise in microphone data, which has good practicability for increasing SNR of aeroacoustic signal. A localization experiment was conducted by using two loudspeakers in wind tunnel with 80 m/s velocity. Results show that proposed method can filter out the background noise more effectively and improve the SNR of the loudspeakers signal compared with spectral subtraction and cepstrum methods. Moreover, the aeroacoustic field produced by a NACA EPPLER 862 STRUT airfoil model was also measured and reconstructed. Delay-and-sum beamforming maps of aeroacoustic source were displayed after the background noise was suppressed, which further demonstrates the proposed method’s advantage.

Cross-correlation based fisheries stock assessment technique utilized array of multiple acoustic sensors which were equidistant pair. However, at practical implementation of this technique, equal distances among acoustic sensors is sometimes challenging due to different practical phenomenon. Therefore, in this study, we work on this issue and investigated the impact of unequal distances among the acoustic sensors. We found that cross-correlation based technique proved its effectiveness even for the unequal spacing among acoustic sensors. We considered chirp generating species of fish and mammals, i.e., damselfish (Dascyllus aruanus), humpback whales (Megaptera novaeangliae), dugongs (Dugong dugong), etc., species, and three acoustic sensors array for simulation purposes. Some limitations including negligence of multipath interference, assuming the delays to be integer were compromised during simulations.

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.

A hybrid acoustic vector sensor array (HAVSA) is consisted mainly of pressure hydrophones and a few vector sensors within. It can acquire good direction-finding results as well as the ability to discriminate the left-right ambiguity at a considerably low cost, and thus is regarded as a promising novel array configuration. In this paper we have deduced the formula of the directivity of HAVSA, and analyzed its characteristics. Results showed that factors, such as the number and positions of vector sensors utilized in HAVSA, the steering angle, and the signal frequency will change the main lobe width, the height of the side lobes, and the left-right ambiguity elimination performance of the array. Furthermore, the general processing method of the HAVSA for DOA estimation is presented, and conventional beamforming (CBF) and Capon beamforming methods are applied. Results show that the HAVSA has the ability to suppress the left-right ambiguity, and the Capon beamforming outperforms CBF. The present work can provide a good reference for the configuration designing and the algorithm researching of HAVSA.

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.

We present a novel Fast Direct Source Localization (FDSL) approach for acoustic isotropic sensor array applications. Unlike previous approximate maximum likelihood (AML) approach, the proposed FDSL focuses on the phase shift caused by time delays among sensors and obtains an analytical result without exhaustive search for all the possible locations. Using this phase shift model, conventional phase uncertainty problem can be solved by a light-weight linear search in the limited phase space, which greatly reduces the computational complexity of array processing. Theoretical analysis of the FDSL has been proposed and compared with the Cramer Rao Bound (CRB) of the AML approach. Simulations using real bird call data validate the advantage of proposed method.

New technologies, taught towards finding clean and renewable energy sources, must be adapted to each Country and to the conditions where it is desired to be implemented. This project is intended to develop equipment, in order to simulate the variable wind condition. As a result it is acquired a tool, useful in the design of systems to get advantage of wind energy available in Colombia. The system includes a wind tunnel, an electronic velocity controller and the development of a program in LabView easily adaptable. This will allow simulating different conditions both, in the variation of amplitude and frequency associated with wind velocity. The methodology comprises 3 steps; the first is about the development of the wind tunnel setting a wind velocity using as a parameter data obtained from a data base in Bogota. In a second step a LabView program was developed, using as a parameter the highest amplitude variation obtained in the data base; finally in the last step, measurement tests were performed so that experimental data and theoretical data were compared. The results showed that the wind tunnel can simulate wind velocity variations, showing accuracy equal to 96.16% and 92.3% for sampling times of 4 and 6 seconds.

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.