Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 153-156). / One of the long term goals of Autonomous Underwater Vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and expert image interpretation. This thesis proposes a vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target for lower cost-per-vehicle sensing and onboard, fully autonomous classification. The contributions of this thesis include the collection of novel high-quality bistatic data sets around spherical and cylindrical targets in situ during the BayEx'14 and Massachusetts Bay 2014 scattering experiments and the development of a machine learning methodology for classifying target shape and estimating orientation using bistatic amplitude data collected by an AUV. To achieve the high quality, densely sampled 3D bistatic scattering data required by this research, vehicle broadside sampling behaviors and an acoustic payload with precision timed data acquisition were developed. Classification was successfully demonstrated for spherical versus cylindrical targets using bistatic scattered field data collected by the AUV Unicorn as a part of the BayEx'14 scattering experiment and compared to simulated scattering models. The same machine learning methodology was applied to the estimation of orientation of aspect-dependent targets, and was demonstrated by training a model on data from simulation then successfully estimating the orientations of a steel pipe in the Massachusetts Bay 2014 experiment. The final models produced from real and simulated data sets were used for classification and parameter estimation of simulated targets in real time in the LAMSS MOOS-IvP simulation environment. / by Erin Marie Fischell. / Ph. D.

Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 335-357). / This thesis presents a sum-product inference algorithm for in-situ, nonparametric platform navigation called Multi-modal iSAM (incremental smoothing and mapping), for problems of thousands of variables. Our method tracks dominant modes in the marginal posteriors of all variables with minimal approximation error, while suppressing almost all low likelihood modes (in a non-permanent manner) to save computation. The joint probability is described by a non-Gaussian factor graph model. Existing inference algorithms in simultaneous localization and mapping assume Gaussian measurement uncertainty, resulting in complex front-end processes that attempt to deal with non-Gaussian measurements. Existing robustness approaches work to remove "outlier" measurements, resulting heuristics and the loss of valuable information. Track different hypotheses in the system has prohibitive computational cost and and low likelihood hypotheses are permanently pruned. Our approach relaxes the Gaussian only restriction allowing the frontend to defer ambiguities (such as data association) until inference. Probabilistic consensus ensures dominant modes across all measurement information. Our approach propagates continuous beliefs on the Bayes (Junction) tree, which is an efficient symbolic refactorization of the nonparametric factor graph, and approximates the underlying Chapman-Kolmogorov equations. Like the predecessor iSAM2 max-product algorithm [Kaess et al., IJRR 2012], we retain the Bayes tree incremental update property, which allows for tractable recycling of previous computations. Several non-Gaussian measurement likelihood models are introduced, such as ambiguous data association or highly non-Gaussian measurement modalities. In addition, keeping with existing inertial navigation for dynamic platforms, we present a novel continuous-time inertial odometry residual function. Inertial odometry uses preintegration to seamlessly incorporate pure inertial sensor measurements into a factor graph, while supporting retroactive (dynamic) calibration of sensor biases. By centralizing our approach around a factor graph, with the aid of modern starved graph database techniques, concerns from different elements of the navigation ecosystem can be separated. We illustrate with practical examples how various sensing modalities can be combined into a common factor graph framework, such as: ambiguous loop closures; raw beam-formed acoustic measurements; inertial odometry; or conventional Gaussian-only likelihoods (parametric) to infer multi-modal marginal posterior belief estimates of system variables. / by Dehann Fourie. / Ph. D.

Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 177-187). / This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation. / by Michael Jordan Stanway. / Ph.D.

Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 118-131). / As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge - as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to 'heartbeat' status messages, with minimal scientific data available until after recovery. Increasing the challenge, long-distance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible. In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data that warrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices. Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an end-to- end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures. / by Christopher Alden Murphy. / Ph.D.

Thesis: S.M., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013. / Includes bibliographical references (pages 133-141). / Between 2002 and 2011 a single mooring was maintained in the core of the Pacific Water boundary current in the Alaskan Beaufort Sea near 152° W. Using velocity and hydrographic data from six year-long deployments during this time period, we examine the interannual variability of the current. It is found that the volume, heat, and freshwater transport have all decreased drastically over the decade, by more than 80%. The most striking changes have occurred during the summer months. Using a combination of weather station data, atmospheric reanalysis fields, and concurrent shipboard and mooring data from the Chukchi Sea, we investigate the physical drivers responsible for these changes. It is demonstrated that an increase in summertime easterly winds along the Beaufort slope is the primary reason for the drop in transport. The intensification of the local winds has in turn been driven by a strengthening of the summer Beaufort High in conjunction with a deepening of the summer Aleutian Low. Since the fluxes of mass, heat, and freshwater through Bering Strait have increased over the same time period, this raises the question as to the fate of the Pacific water during recent years and its impacts. We present evidence that more heat has been fluxed directly into the interior basin from Barrow Canyon rather than entering the Beaufort shelfbreak jet, and this is responsible for a significant portion of the increased ice melt in the Pacific sector of the Arctic Ocean. / by Eric T. Brugler. / S.M.

Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2014. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 237-243). / This thesis studies the problems associated with adaptive signal processing in the sample deficient regime using random matrix theory. The scenarios in which the sample deficient regime arises include, among others, the cases where the number of observations available in a period over which the channel can be approximated as time-invariant is limited (wireless communications), the number of available observations is limited by the measurement process (medical applications), or the number of unknown coefficients is large compared to the number of observations (modern sonar and radar systems). Random matrix theory, which studies how different encodings of eigenvalues and eigenvectors of a random matrix behave, provides suitable tools for analyzing how the statistics estimated from a limited data set behave with respect to their ensemble counterparts. The applications of adaptive signal processing considered in the thesis are (1) adaptive beamforming for spatial spectrum estimation, (2) tracking of time-varying channels and (3) equalization of time-varying communication channels. The thesis analyzes the performance of the considered adaptive processors when operating in the deficient sample support regime. In addition, it gains insights into behavior of different estimators based on the estimated second order statistics of the data originating from time-varying environment. Finally, it studies how to optimize the adaptive processors and algorithms so as to account for deficient sample support and improve the performance. In particular, random matrix quantities needed for the analysis are characterized in the first part. In the second part, the thesis studies the problem of regularization in the form of diagonal loading for two conventionally used spatial power spectrum estimators based on adaptive beamforming, and shows the asymptotic properties of the estimators, studies how the optimal diagonal loading behaves and compares the estimators on the grounds of performance and sensitivity to optimal diagonal loading. In the third part, the performance of the least squares based channel tracking algorithm is analyzed, and several practical insights are obtained. Finally, the performance of multi-channel decision feedback equalizers in time-varying channels is characterized, and insights concerning the optimal selection of the number of sensors, their separation and constituent filter lengths are presented. / by Milutin Pajovic. / Ph. D.

Thesis: S.M., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 79-81). / The ocean is a complex, constantly changing, highly dynamical system. Prediction capabilities are constantly being improved in order to better understand and forecast ocean properties for applications in science, industry, and maritime interests. Our overarching goal is to better predict the ocean environment in regions of complex topography with a continental shelf, shelfbreak, canyons and steep slopes using the MIT Multidisciplinary Simulation, Estimation and Assimilation Systems (MSEAS) primitive-equation ocean model. We did this by focusing on the complex region surrounding Taiwan, and the period of time immediately following the passage of Typhoon Morakot. This area and period were studied extensively as part of the intense observation period during August - September 2009 of the joint U.S. - Taiwan program Quantifying, Predicting, and Exploiting Uncertainty Department Research Initiative (QPE DRI). Typhoon Morakot brought an unprecedented amount of rainfall within a very short time period and in this research, we model and study the effects of this rainfall on Taiwan's coastal oceans as a result of river discharge. We do this through the use of a river discharge model and a bulk river-ocean mixing model. We complete a sensitivity study of the primitive-equation ocean model simulations to the different parameters of these models. By varying the shape, size, and depth of the bulk mixing model footprint, and examining the resulting impacts on ocean salinity forecasts, we are able to determine an optimal combination of salinity relaxation factors for highest accuracy. / by Jennifer Jacobs Landry. / S.M.

Thesis: Ph. D., Joint Program in Chemical Oceanography (Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Tracing the evolution of Earth's redox history is one of the great challenges of geobiology and geochemistry. The accumulation of photosynthetically derived oxygen transformed the redox state of Earth's surface environments, setting the stage for the subsequent evolution of complex life. However, the timing of the advent of oxygenic photosynthesis relative to the Great Oxidation Event (GOE; -2.4 Ga) is poorly constrained. After the deep ocean became oxygenated in the early Phanerozoic, hydrogen sulfide, which is toxic to most aerobes, may have transiently accumulated in the marine photic zone (i.e. photic zone euxinia; PZE) during mass extinctions and oceanic anoxic events. Here, the molecular fossil evidence for oxygenic photosynthesis and eukaryotes is reevaluated, where the results imply that currently existing lipid biomarkers are contaminants. Next, the stratigraphic distribution of green and purple sulfur bacteria biomarkers through geologic time is evaluated to test whether these compounds reflect a water column sulfide signal, which is implicit in their utility as PZE paleoredox proxies. Results from a modern case study underscore the need to consider allochthonous and microbial mat sources and the role of basin restriction as alternative explanations for these biomarkers in the geologic record, in addition to an autochthonous planktonic source. / by Katherine L. French. / Ph. D.

Thesis (M.S.)--Joint Program in Oceanography (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), February 2001. / Includes bibliographical references (leaves 144-149). / A bi-weekly seasonal study was conducted in Ashumet Pond (Cape Cod, Massachusetts). The Redfield Ratio (106C:16N:1P) and alkaline phosphatase activity (APA) were utilized in tandem as nutrient deficiency indicators (NDIs) for phytoplankton. The study objective was to evaluate the limiting nutrient status of the pond throughout the growing season. The development of a high throughput method for fluorometrically measuring APA allowed for a large quantity of pond-water samples to be analyzed. The new method utilized a cytofluor, a fluorescence multi-well plate reader, which increased sample throughput by 75% compared to a standard filter fluorometer method. The detection limit, capability to measure APA at different time intervals, and performance at sea were tested. APA measurements made using the cytofluor were comparable to those made using a standard filter fluorometer, thus indicating that the cytofluor is a suitable and preferred replacement to the fluorometer for APA measurements. The presence of alkaline phosphatase, an inducible phospho-hydrolytic enzyme, is commonly used as an NDI diagnostic for phosphate limitation. A nutrient enrichment incubation re-affirmed the use of APA as a robust indicator of phosphate limitation in phytoplankton. APA data indicate that the system experienced episodic periods of phosphate-deficiency, implying that the limiting nutrient regime was not static, but was changeable throughout the growing season. Seasonal trends in dissolved N:P and particulate C:P ratios often contradict the APA results, however, suggesting that the Redfield Ratio is an unreliable indicator of the overall nutrient limitation regime of the pond. The observed discrepancies between C:N:P and APA can be reconciled by taking into account seasonal changes in species composition, which played an important role in driving seasonal APA trends. / by Christie Lynn Haupert. / M.S.

Thesis (Ph. D.)--Joint Program in Oceanography (Massachusetts Institute of Technology, Dept. of Biology; and the Woods Hole Oceanographic Institution), 1999. / Includes bibliographical references (leaves 196-210). / by Susan M. Bello. / Ph.D.