Thesis (M.S. in Engineering Acoustics)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Steven R. Baker, Thomas G. Muir. Includes bibliographical references (p. 89-90). Also available online.

No description available.

Underwater acoustic modelling is an important aspect of Synthetic Aperture Sonar (SAS) system design and algorithm development. Sea-trials are an expensive and time-consuming exercise and simulations provide an efficient and economic alternative. However, there are few simulators (in the open literature) that can efficiently provide realistic SAS data for large, complicated scenes. Conventional side-scan sonar simulators are not suitable for SAS data simulation. These simulators utilise narrow-beam and narrow-band approximations; typical SAS systems are wide-beam and wide-band and these approximations are invalid. Moreover, conventional side-scan sonar is a non-coherent imaging technique and SAS processing relies on the phase. Existing SAS simulators are capable of modelling very simple scenes only. They utilise a decomposition of the scene into point or smooth facet primitives, which is very inefficient. Many primitives are required and this imposes a severe restriction on scene complexity and size. This thesis presents a rigorous mathematical framework for the modelling of SAS imagery. A novel acoustic scattering model is developed and its implementation in a wide-beam and wide-band, multiple-receiver Interferometric SAS (InSAS) simulator is detailed. The scattering model utilises a decomposition of the scene into rough (rather than smooth) facet primitives. The use of rough facet primitives provides a significant increase in computational efficiency since scenes are decomposed into fewer primitives. This facilitates the simulation of larger and more complicated scenes. Each rough facet is characterised by its far-field beampattern. The statistics of the beampattern are related to the facet shape and roughness statistics using the Kirchhoff approximation. The beampattern is realised from its first and second-order statistics. The SAS imagery is obtained using a coherent sum of the facet responses and occlusions and multiple-scattering are resolved by ray-tracing. The simulator is implemented for use on a parallel computing cluster. The simulator is shown to provide realistic SAS data that is qualitatively and quantitatively similar to real data. The simulated results are considered, in many ways, superior to the simulated results in the literature.

The use of multi-sensor systems entails a Data Fusion and Sensor Management requirement in order to optimize the use of resources and allow the synergistic operation of sensors. To date, data fusion and sensor management have largely been dealt with separately and primarily for centralized and hierarchical systems. Although work has recently been done in distributed and decentralized data fusion, very little of it has addressed sensor management. In decentralized systems, a consistent and coherent approach is essential and the ad hoc methods used in other systems become unsatisfactory. This thesis concerns the development of a unified approach to data fusion and sensor management in multi-sensor systems in general and decentralized systems in particular, within a single consistent information-theoretic framework. Our approach is based on considering information and its gain as the main goal of multi-sensor systems. We develop a probabilistic information update paradigm from which we derive directly architectures and algorithms for decentralized data fusion and, most importantly, address sensor management. Presented with several alternatives, the question of how to make decisions leading to the best sensing configuration or actions, defines the management problem. We discuss the issues in decentralized decision making and present a normative method for decentralized sensor management based on information as expected utility. We discuss several ways of realizing the solution culminating in an iterative method akin to bargaining for a general decentralized system. Underlying this is the need for a good sensor model detailing a sensor's physical operation and the phenomenological nature of measurements vis-a-vis the probabilistic information the sensor provides. Also, implicit in a sensor management problem is the existence of several sensing alternatives such as those provided by agile or multi-mode sensors. With our application in mind, we detail such a sensor model for a novel Tracking Sonar with precisely these capabilities making it ideal for managed data fusion. As an application, we consider vehicle navigation, specifically localization and map-building. Implementation is on the OxNav vehicle (JTR) which we are currently developing. The results show, firstly, how with managed data fusion, localization is greatly speeded up compared to previous published work and secondly, how synergistic operation such as sensor-feature assignments, hand-off and cueing can be realised decentrally. This implementation provides new ways of addressing vehicle navigation, while the theoretical results are applicable to a variety of multi-sensing problems.

No description available.

The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. These include, for example, mining minerals, inspecting pipeline and mapping oceans, sampling in contaminated water. Also, there has been another growing interest for security forces in precluding submarines or intruders from a beach or harbour entrance as well as hunting shallow water mines. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. Since accurate surrounding information is essential in order to manoeuvre the AUV efficiently and economically, while corrupt information can jeopardize an entire mission. By extracting the space information form sensors, an AUV can achieve the localisation and mapping which are currently two primary concerns in the robotics research. Meanwhile, such information will provide a fundament of protection for surface vessels or troops, harbour infrastructure and oil plant against the enemy and terrorism. Acoustic sensors are commonly used to detect and position underwater obstacles, suspicious objects or to map the surroundings because sound waves can propagate more appreciable distances than electromagnetic and optical energy in the water. The measurements from these sensors, however, are always bound up with noises and errors. Various underwater activities may further pollute sound signals and then threaten the AUV navigation process. To simplify the detection procedure, some researchers make use of acoustic beacons or apparent obstructions (such as rocks, concrete walls) because they have distinctive characteristics. Point or line features are extracted from the acoustic signals or images for localization and mapping purposes. The long propagation range of sound waves can present new problems when acoustic sensors operate in confined environments, such as water tanks, rivers and harbours. The multiple reflections will be recorded by the sensor and result in false alarms. Furthermore, with advances in manufacturing techniques, the downsizing in marine explosive ordnances is progressing significantly, making it more difficult to discriminate between surface reflections and explosive ordnances. Finally, under the consideration of cost effectiveness, a mechanically scanned sonar has been introduced for the AUV in this research. However, the sensor beam cannot cover a large region simultaneously and a moving object may be distorted in the acoustic image because of the relatively low scanning speed. Due to such distortions in the data flows, objects may be indistinguishable from random noise or reverberation in acoustic images. The research presented here addresses the afore-mentioned problems relating to the theme of automatic detection from acoustic images. It is concerned with the detection and tracking of small underwater objects in order to protect autonomous underwater vehicles using sonar (SOund Navigation and Range). In the present study, these vehicles operated in laboratory water tanks or natural river environments. This research made use of self provided analytical studies that differentiated between reverberation and real object echoes. Detections were achieved automatically by using signal and image processing techniques. This research consists of three important and linked strategies. Firstly, a simple and fast reverberation suppression filter was provided, based on the understanding of the mechanism of the sonar sensor. Secondly, a robust detection system was developed to perceive small suspended obstacles in the water. Thirdly and finally, arc features were successfully extracted from the acoustic images and mathematical maps were generated from those features. The majority of experiments were derived from the elliptical water tank and the River Torrens, Adelaide, South Australia. For this project, a sequence of sonar images was taken from the same sonar location in the elliptical water tank. Further, a sequence of sonar images was taken from a sequence of sonar locations in the natural river. They provided different data sets for the assessment and evaluation of self developed algorithms. Results shown in this thesis confirm the favourable outcomes of the investigation and applied methodology. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1454839 / Thesis (M.Eng.Sc.) -- University of Adelaide, School of Mechanical Engineering, 2010

Thesis (Ph. D.)--Ohio University, June, 1990. / Title from PDF t.p.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2009. / Thesis Advisor(s): Horner, Doug ; Cristi, Roberto. "March 2009." Description based on title screen as viewed on May 6, 2009. Author(s) subject terms: Unmanned Vehicles, Forward Looking Sonar, REMUS, UUV, Occupancy Grid, 3D Reconstruction, Three-Dimensional Mapping Includes bibliographical references (p. 63-64). Also available in print.

Originally presented as the author's thesis (master's, University of Rhode Island). / Cover title. "27 April 1967." Includes bibliographical references (leaf 54).

Thesis (M.S. in Meteorology and Physical Oceanography) Naval Postgraduate School, June 1995. / Thesis advisor(s): James H. Wilson, Robert H. Bourke. "NPS-OC-95-002." "June 1995." Bibliography: p. 175-188. Also available online.