Use of beamforming for detecting an acoustic source inside a cylindrical shell filled with a heavy fluid

International audienceThe acoustic detection of defects or leaks inside a cylindrical shell containing a fluid is of prime importance in the industry, particularly in the nuclear field. This paper examines the beamforming technique which is used to detect and locate the presence of an acoustic monopole inside a cylindrical elastic shell by measuring the external shell vibrations. In order to study the effect of fluid-structure interactions and the distance of the source from the array of sensors, a vibro-acoustic model of the fluid-loaded shell is first considered for numerical experiments. The beamforming technique is then applied to radial velocities of the shell calculated with the model. Different parameters such as the distance between sensors, the radial position of the source, the damping loss factor of the shell, or of the fluid, and modifications of fluid properties can be considered without difficulty. Analysis of thes

A Methodology for Acoustic Measurement and Separation of Background Noise

An attempt is made at developing experimental methods for the acoustic measurement and separation of background noise in a wind tunnel. To this end, an array beamforming technique known as delay-and- sum beamforming is identified and tested. The theory underlying delay-and-sum beamforming is discussed. Two linear arrays, the seven microphone linear array and the four microphone minimum redundancy array, are designed. A driver is designed based on Helmholtz resonator theory to provide a source of monochromatic sound. Also, the concept of partial coherence as applicable to the separation of background noise from signal noise is investigated. Array beamforming results show that tests conducted with the two linear arrays in the open field provide good resolution of the sound source Direction Of Arrival (DOA) peaks from the background noise, and provide a semianechoic reference with which to compare wind tunnel results. Beamforming results obtained for the driver placed inside the wind tunnel with the tunnel running at 0, 45, and 81 ft/sec successfully resolved the DOA peaks of the driver from the background noise of the tunnel. At a tunnel velocity of 151 ft/sec, the driver signal is completely buried in the background noise of the tunnel, and beamforming was not successful in resolving the peak corresponding to the driver signal

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets

Analysis of Vector Sensor Data Collected in Gulf of Mexico

In 2015, the Naval Oceanographic Office collected vector sensor data in approximately 100 meters of water southwest of Panama City, Florida in the Gulf of Mexico. The vector sensor was deployed at a center mass height of one foot above the seafloor and de-coupled from its mooring through lightweight springs to measure local acoustical pressure and particle velocity. Accuracy of the data across frequency and source azimuth is measured by evaluating acoustical impedance as a function of frequency and source azimuthal direction. Results indicate the vector sensor has an effective band from 50 to 450 Hz with mooring reflections and resonances degrading performance above this band. Localization using three spatial processing methods are analyzed for high and low Signal to Noise Ratio (SNR) sources. Directional accuracy is approximately 3 degrees up to 350 Hz and 10 degrees above 350 Hz. Noise sources from air guns, ships, and mammals are spatially processed and the results show that the vector sensor is capable of discriminating the location of two high SNR sources in the environment that are sufficiently separated in either location, time, or frequency

Unattended acoustic sensor systems for noise monitoring in national parks

2017 Spring.Includes bibliographical references.Detection and classification of transient acoustic signals is a difficult problem. The problem is often complicated by factors such as the variety of sources that may be encountered, the presence of strong interference and substantial variations in the acoustic environment. Furthermore, for most applications of transient detection and classification, such as speech recognition and environmental monitoring, online detection and classification of these transient events is required. This is even more crucial for applications such as environmental monitoring as it is often done at remote locations where it is unfeasible to set up a large, general-purpose processing system. Instead, some type of custom-designed system is needed which is power efficient yet able to run the necessary signal processing algorithms in near real-time. In this thesis, we describe a custom-designed environmental monitoring system (EMS) which was specifically designed for monitoring air traffic and other sources of interest in national parks. More specifically, this thesis focuses on the capabilities of the EMS and how transient detection, classification and tracking are implemented on it. The Sparse Coefficient State Tracking (SCST) transient detection and classification algorithm was implemented on the EMS board in order to detect and classify transient events. This algorithm was chosen because it was designed for this particular application and was shown to have superior performance compared to other algorithms commonly used for transient detection and classification. The SCST algorithm was implemented on an Artix 7 FPGA with parts of the algorithm running as dedicated custom logic and other parts running sequentially on a soft-core processor. In this thesis, the partitioning and pipelining of this algorithm is explained. Each of the partitions was tested independently to very their functionality with respect to the overall system. Furthermore, the entire SCST algorithm was tested in the field on actual acoustic data and the performance of this implementation was evaluated using receiver operator characteristic (ROC) curves and confusion matrices. In this test the FPGA implementation of SCST was able to achieve acceptable source detection and classification results despite a difficult data set and limited training data. The tracking of acoustic sources is done through successive direction of arrival (DOA) angle estimation using a wideband extension of the Capon beamforming algorithm. This algorithm was also implemented on the EMS in order to provide real-time DOA estimates for the detected sources. This algorithm was partitioned into several stages with some stages implemented in custom logic while others were implemented as software running on the soft-core processor. Just as with SCST, each partition of this beamforming algorithm was verified independently and then a full system test was conducted to evaluate whether it would be able to track an airborne source. For the full system test, a model airplane was flown at various trajectories relative to the EMS and the trajectories estimated by the system were compared to the ground truth. Although in this test the accuracy of the DOA estimates could not be evaluated, it was show that the algorithm was able to approximately form the general trajectory of a moving source which is sufficient for our application as only a general heading of the acoustic sources is desired

A comparison between aeroacoustic source mapping techniques for the characterisation of wind turbine blade models with microphone arrays

Characterising the aeroacoustic noise sources generated by a rotating wind turbine blade provides useful information for tackling noise reduction of this mechanical system. In this context, microphone array measurements and acoustic source mapping techniques are powerful tools for the identification of aeroacoustic noise sources. This paper discusses a series of acoustic mapping strategies that can be exploited in this kind of applications. A single-blade rotor was tested in a semi-anechoic chamber using a circular microphone array. The Virtual Rotating Array (VRA) approach, which transforms the signals acquired by the physical static array into signals of virtual microphones synchronously rotating with the blade, hence ensuring noise-source stationarity, was used to enable the use of frequency domain acoustic mapping techniques. A comparison among three different acoustic mapping methods is presented: Conventional Beamforming, CLEAN-SC and Covariance Matrix Fitting based on Iterative Re-weighted Least Squares and Bayesian approach. The latter demonstrated to provide the best results for the application and made it possible a detailed characterization of the noise sources generated by the rotating blade at different operating conditions