ACOUSTIC LOCALIZATION TECHNIQUES FOR APPLICATION IN NEAR-SHORE ARCTIC ENVIRONMENTS

The Arctic environment has undergone significant change in recent years. Multi-year ice is no longer prevalent in the Arctic. Instead, Arctic ice melts during summer months and re-freezes each winter. First-year ice, in comparison to multi-year ice, is different in terms of its acoustic properties. Therefore, acoustic propagation models of the Arctic may no longer be valid. The open water in the Arctic for longer time periods during the year invites anthropogenic traffic such as civilian tourism, industrial shipping, natural resource exploration, and military exercises. It is important to understand sound propagation in the first-year ice environment, especially in near-shore and shallow-water regions, where anthropogenic sources may be prevalent. It is also important to understand how to detect, identify, and track the anthropogenic sources in these environments in the absence of large acoustic sensory arrays. The goals of this dissertation are twofold: 1) Provide experimental transmission loss (TL) data for the Arctic environment as it now exists, that it may be used to validate new propagation models, and 2) Develop improved understanding of acoustic vector sensor (AVS) performance in real-world applications such as the first-year Arctic environment. Underwater and atmospheric acoustic TL have been measured in the Arctic environment. Ray tracing and parabolic equation simulations have been used for comparison to the TL data. Generally good agreement is observed between the experimental data and simulations, with some discrepancies. These discrepancies may be eliminated in the future with the development of improved models. Experiments have been conducted with underwater pa and atmospheric pp AVS to track mechanical noise sources in real-world environments with various frequency content and signal to noise ratio (SNR). A moving standard deviation (MSD) processing routine has been developed for use with AVS. The MSD processing routine is shown to be superior to direct integration or averaging of intensity spectra for direction of arrival (DOA) estimation. DOA error has been shown to be dependent on ground-reflected paths for pp AVS with analytical models. Underwater AVS have been shown to be feasible to track on-ice sources and atmospheric AVS have been shown feasible to track ground vehicle sources

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

ON-ICE DETECTION, CLASSIFICATION, LOCALIZATION AND TRACKING OF ANTHROPOGENIC ACOUSTIC SOURCES WITH MACHINE LEARNING

Arctic acoustics have been of concern in recent years for the US navy. First-year ice is now the prevalent factor in ice coverage in the Arctic, which changes the previously understood acoustic properties. Due to the ice melting each year, anthropogenic sources in the Arctic region are more common: military exercises, shipping, and tourism. For the navy, it is of interest to detect, classify, localize, and track these sources to have situational awareness of these surroundings. Because the sources are on-water or on-ice, acoustic radiation propagates at a longer distance and so acoustics are the method by which the sources are detected, classified, localized, and tracked. These methods are all part of sound navigation and ranging (SONAR). This dissertation describes algorithms which will better SONAR results without modification of the sensors or the environment and the process by which to arrive to this point. The focus is to use supervised machine learning algorithms to facilitate such technological enhancements. Specifically, neural networks analyze labeled experimental data from a first-year, shore-fast, shallow and narrow water environment. The experiments were conducted over the span of three years from 2019 to 2022, mostly during the months from January to March where ice formed over the Keweenaw Waterway at the Michigan Technological University. All experiments were conducted to analyze a passive acoustic source; that is, the source was non-cooperative and did not send any localizing pings for active SONAR. The experiments were recorded using an underwater pa-type acoustic vector sensor (AVS). The data and analysis were done intermittently to update any upcoming experiments with discrepancies found in the analysis to create a more generalized algorithm. The work in this dissertation focuses on two topics for passive SONAR: localization and classification. Because of the ``black box nature in machine learning, tracking the target source is an extension of localization and thought of as the same goal within machine learning. To introduce and verify the complexity of the testing environment, an underwater acoustic simulation is shown with Ray tracing and bathymetry data to compare with the experimental results used in machine learning. The focus of the algorithms is to produce the best results for the experiments and compare the results with traditional methods, such as a simulation or a linear Gaussian localization with a Kalman filter. Experiments studying neural network types have shown that the Vision Transformer (ViT) produces excellent results. The ViT is capable of analyzing acoustic intensity azimuthal spectrogram (azigram) data and localizing a moving target at high accuracy, and the ViT is capable of classifying multiple acoustic sources with the acoustic intensity magnitude spectrogram at high accuracy as well

Cooperative Localization in Mobile Underwater Acoustic Sensor Networks

Die großflächige Erkundung und Überwachung von Tiefseegebieten gewinnt mehr und mehr an Bedeutung für Industrie und Wissenschaft. Diese schwer zugänglichen Areale in der Tiefsee können nur mittels Teams unbemannter Tauchbote effizient erkundet werden. Aufgrund der hohen Kosten, war bisher ein Einsatz von mehreren autonomen Unterwasserfahrzeugen (AUV) wirtschaftlich undenkbar, wodurch AUV-Teams nur in Simulationen erforscht werden konnten. In den letzten Jahren konnte jedoch eine Entwicklung hin zu günstigeren und robusteren AUVs beobachtet werden. Somit wird der Einsatz von AUV-Teams in Zukunft zu einer realen Option. Die wachsende Nachfrage nach Technologien zur Unterwasseraufklärung und Überwachung konnte diese Entwicklung noch zusätzlich beschleunigen. Eine der größten technischen Hürden für tief tauchende AUVs ist die Unterwasserlokalisierug. Satelitengestützte Navigation ist in der Tiefe nicht möglich, da Radiowellen bereits nach wenigen Metern im Wasser stark an Intensität verlieren. Daher müssen neue Ansätze für die Unterwasserlokalisierung entwickelt werden die sich auch für Fahrzeugenverbände skalieren lassen. Der Einsatz von AUV-Teams ermöglicht nicht nur völlig neue Möglichkeiten der Kooperation, sondern erlaubt auch jedem einzelnen AUV von den Navigationsdaten der anderen Fahrzeuge im Verband zu profitieren, um die eigene Lokalisierung zu verbessern. In dieser Arbeit wird ein kooperativer Lokalisierungsansatz vorgestellt, welcher auf dem Nachrichtenaustausch durch akustische Ultra-Short Base-Line (USBL) Modems basiert. Ein akustisches Modem ermöglicht die Übertragung von Datenpaketen im Wasser, wärend ein USBL-Sensor die Richtung einer akustischen Quelle bestimmen kann. Durch die Kombination von Modem und Sensor entsteht ein wichtiges Messinstrument für die Unterwasserlokalisierung. Wenn ein Fahrzeug ein Datenpaket mit seiner eignen Position aussendet, können andere Fahrzeuge mit einem USBL-Modem diese Nachricht empfangen. In Verbindung mit der Richtungsmessung zur Quelle, können diese Daten von einem Empfangenden AUV verwendet werden, um seine eigene Positionsschatzung zu verbessern. Diese Arbeit schlägt einen Ansatz zur Fusionierung der empfangenen Nachricht mit der Richtungsmessung vor, welcher auch die jeweiligen Messungenauigkeiten berücksichtigt. Um die Messungenauigkeit des komplexen USBL-Sensors bestimmen zu können, wurde zudem ein detailliertes Sensormodell entwickelt. Zunächst wurden existierende Ansätze zur kooperativen Lokalisierung (CL) untersucht, um daraus eine Liste von erwünschten Eigenschaften für eine CL abzuleiten. Darauf aufbauend wurde der Deep-Sea Network Lokalisation (DNL) Ansatz entwickelt. Bei DNL handelt es sich um eine CL Methode, bei der die Skalierbarkeit sowie die praktische Anwendbarkeit im Fokus stehen. DNL ist als eine Zwischenschicht konzipiert, welche USBL-Modem und Navigationssystem miteinander verbindet. Es werden dabei Messwerte und Kommunikationsdaten des USBL zu einer Standortbestimmung inklusive Richtungsschätzung fusioniert und an das Navigationssystem weiter geleitet, ähnlich einem GPS-Sensor. Die Funktionalität von USBL-Modell und DNL konnten evaluiert werden anhand von Messdaten aus Seeerprobungen in der Ostsee sowie im Mittelatlantik. Die Qualität einer CL hangt häufig von vielen unterschiedlichen Faktoren ab. Die Netzwerktopologie muss genauso berücksichtig werden wie die Lokalisierungsfähigkeiten jedes einzelnen Teilnehmers. Auch das Kommunikationsverhalten der einzelnen Teilnehmer bestimmt, welche Informationen im Netzwerk vorhanden sind und hat somit einen starken Einfluss auf die CL. Um diese Einflussfaktoren zu untersuchen, wurden eine Reihe von Szenarien simuliert, in denen Kommunikationsverhalten und Netzwerktopologie für eine Gruppe von AUVs variiert wurden. In diesen Experimenten wurden die AUVs durch ein Oberflächenfahrzeug unterstützt, welches seine geo-referenzierte Position über DNL an die getauchten Fahrzeuge weiter leitete. Anhand der untersuchten Topologie können die Experimente eingeteilt werden in Single-Hop und Multi-Hop. Single-Hop bedeutet, dass jedes AUV sich in der Sendereichweite des Oberflächenfahrzeugs befindet und dessen Positionsdaten auf direktem Wege erhält. Wie die Ergebnisse der Single-Hop Experimente zeigen, kann der Lokalisierungsfehler der AUVs eingegrenzt werden, wenn man DNL verwendet. Dabei korreliert der Lokalisierungsfehler mit der kombinierten Ungenauigkeit von USBL-Messung und Oberflächenfahrzeugposition. Bei den Multi-Hop Experimenten wurde die Topologie so geändert, dass sich nur eines der AUVs in direkter Sendereichweite des Oberflächenfahrzeugs befindet. Dieses AUV verbessert seine Position mit den empfangen Daten des Oberflächenfahrzeugs und sendet wiederum seine verbesserte Position an die anderen AUVs. Auch hier konnte gezeigt werden, dass sich der Lokalisierungfehler der Gruppe mit DNL einschränken lässt. Ändert man nun das Schema der Kommunikation so, dass alle AUVs zyklisch ihre Position senden, zeigte sich eine Verschlechterung der Lokalisierungsqualität der Gruppe. Dieses unerwartet Ergebnis konnte auf einen Teil des DNL-Algorithmus zurück geführt werden. Da die verwendete USBL-Klasse nur die Richtung eines Signals misst, nicht jedoch die Entfernung zum Sender, wird in der DNL-Schicht eine Entfernungsschatzung vorgenommen. Wenn die Kommunikation nicht streng unidirektional ist, entsteht eine Ruckkopplungsschleife, was zu fehlerhaften Entfernungsschatzungen führt. Im letzten Experiment wird gezeigt wie sich dieses Problem vermeiden lasst, mithilfe einer relativ neue USBL-Klasse, die sowohl Richtung als auch Entfernung zum Sender misst. Die zwei wesentlichen Beiträge dieser Arbeit sind das USBL-Model zum einen und zum Anderen, der neue kooperative Lokalisierungsansatz DNL. Mithilfe des Sensormodels lassen sich nicht nur Messabweichungen einer USBL-Messung bestimmen, es kann auch dazu genutzt werden, einige Fehlereinflüsse zu korrigieren. Mit DNL wurde eine skalierbare CL-Methode entwickelt, die sich gut für den den Einsatz bei mobilen Unterwassersensornetzwerken eignet. Durch das Konzept als Zwischenschicht, lasst sich DNL einfach in bestehende Navigationslösungen integrieren, um die Langzeitstabilität der Navigation für große Verbände von tiefgetauchten Fahrzeugen zu gewährleisten. Sowohl USBL-Model als auch DNL sind dabei so ressourcenschonend, dass sie auf dem Computer eines Standard USBL laufen können, ohne die ursprüngliche Funktionalität einzuschränken, was den praktischen Einsatz zusätzlich vereinfacht

Detection, source location, and analysis of volcano infrasound

Thesis (Ph.D.) University of Alaska Fairbanks, 2017The study of volcano infrasound focuses on low frequency sound from volcanoes, how volcanic processes produce it, and the path it travels from the source to our receivers. In this dissertation we focus on detecting, locating, and analyzing infrasound from a number of different volcanoes using a variety of analysis techniques. These works will help inform future volcano monitoring using infrasound with respect to infrasonic source location, signal characterization, volatile flux estimation, and back-azimuth to source determination. Source location is an important component of the study of volcano infrasound and in its application to volcano monitoring. Semblance is a forward grid search technique and common source location method in infrasound studies as well as seismology. We evaluated the effectiveness of semblance in the presence of significant topographic features for explosions of Sakurajima Volcano, Japan, while taking into account temperature and wind variations. We show that topographic obstacles at Sakurajima cause a semblance source location offset of ~360-420 m to the northeast of the actual source location. In addition, we found despite the consistent offset in source location semblance can still be a useful tool for determining periods of volcanic activity. Infrasonic signal characterization follows signal detection and source location in volcano monitoring in that it informs us of the type of volcanic activity detected. In large volcanic eruptions the lowermost portion of the eruption column is momentum-driven and termed the volcanic jet or gas-thrust zone. This turbulent fluid-flow perturbs the atmosphere and produces a sound similar to that of jet and rocket engines, known as jet noise. We deployed an array of infrasound sensors near an accessible, less hazardous, fumarolic jet at Aso Volcano, Japan as an analogue to large, violent volcanic eruption jets. We recorded volcanic jet noise at 57.6° from vertical, a recording angle not normally feasible in volcanic environments. The fumarolic jet noise was found to have a sustained, low amplitude signal with a spectral peak between 7-10 Hz. From thermal imagery we measure the jet temperature (~260 °C) and estimate the jet diameter (~2.5 m). From the estimated jet diameter, an assumed Strouhal number of 0.19, and the jet noise peak frequency, we estimated the jet velocity to be ~79 - 132 m/s. We used published gas data to then estimate the volatile flux at ~160 - 270 kg/s (14,000 - 23,000 t/d). These estimates are typically difficult to obtain in volcanic environments, but provide valuable information on the eruption. At regional and global length scales we use infrasound arrays to detect signals and determine their source back-azimuths. A ground-coupled airwave (GCA) occurs when an incident acoustic pressure wave encounters the Earth's surface and part of the energy of the wave is transferred to the ground. GCAs are commonly observed from sources such as volcanic eruptions, bolides, meteors, and explosions. They have been observed to have retrograde particle motion. When recorded on collocated seismo-acoustic sensors, the phase between the infrasound and seismic signals is 90°. If the sensors are separated wind noise is usually incoherent and an additional phase is added due to the sensor separation. We utilized the additional phase and the characteristic particle motion to determine a unique back-azimuth solution to an acoustic source. The additional phase will be different depending on the direction from which a wave arrives. Our technique was tested using synthetic seismo-acoustic data from a coupled Earth-atmosphere 3D finite difference code and then applied to two well-constrained datasets: Mount St. Helens, USA, and Mount Pagan, Commonwealth of the Northern Mariana Islands Volcanoes. The results from our method are within ~<1° - 5° of the actual and traditional infrasound array processing determined back-azimuths. Ours is a new method to detect and determine the back-azimuth to infrasonic signals, which will be useful when financial and spatial resources are limited

The sound of a Martian dust devil

Dust devils (convective vortices loaded with dust) are common at the surface of Mars, particularly at Jezero crater, the landing site of the Perseverance rover. They are indicators of atmospheric turbulence and are an important lifting mechanism for the Martian dust cycle. Improving our understanding of dust lifting and atmospheric transport is key for accurate simulation of the dust cycle and for the prediction of dust storms, in addition to being important for future space exploration as grain impacts are implicated in the degradation of hardware on the surface of Mars. Here we describe the sound of a Martian dust devil as recorded by the SuperCam instrument on the Perseverance rover. The dust devil encounter was also simultaneously imaged by the Perseverance rover's Navigation Camera and observed by several sensors in the Mars Environmental Dynamics Analyzer instrument. Combining these unique multi-sensorial data with modelling, we show that the dust devil was around 25m large, at least 118m tall, and passed directly over the rover travelling at approximately 5ms-1. Acoustic signals of grain impacts recorded during the vortex encounter provide quantitative information about the number density of particles in the vortex. The sound of a Martian dust devil was inaccessible until SuperCam microphone recordings. This chance dust devil encounter demonstrates the potential of acoustic data for resolving the rapid wind structure of the Martian atmosphere and for directly quantifying wind-blown grain fluxes on Mars.We are most grateful for the support of the Mars 2020 project team, including hardware and operation teams. This project was supported in the US by the NASA Mars Exploration Program, and in France by CNES. It is based on observations with SuperCam embarked on Perseverance (Mars2020). The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, is under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The JPL co-author (M.T.) acknowledges funding from NASA’s Space Technology Mission Directorate and the Science Mission Directorate. A. V-R is supported by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia “María de Maeztu”- Centro de Astrobiología (INTA-CSIC), and by the Comunidad de Madrid Project S2018/NMT-4291 (TEC2SPACE-CM). R.H. and A.S-L. were supported by Grant PID2019-109467GB-I00 funded by MCIN/AEI/10.13039/501100011033/ and by Grupos Gobierno Vasco IT1742-22. A.M. was supported by Grant PRE2020-092562 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. R.L. acknowledges InSight PSP Grant 80NSSC18K1626 as well as the Mars 2020 project. B.C. is supported by the Director’s Postdoctoral Fellowship from the Los Alamos National Laboratory, grant 20210960PRD3. JA.RM., M.M, J.T and J.G-E were supported by MCIN/AEI’s Grant RTI2018-098728-B-C31

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Signal Processing and Propagation for Aeroacoustic Sensor Networking,” Ch

Passive sensing of acoustic sources is attractive in many respects, including the relatively low signal bandwidth of sound waves, the loudness of most sources of interest, and the inherent difficulty of disguising or concealing emitted acoustic signals. The availability of inexpensive, low-power sensing and signal-processing hardware enables application of sophisticated real-time signal processing. Among th

Aeronautical Engineering: A special bibliography with indexes, supplement 55

This bibliography lists 260 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1975