A proposed technique for source localization using an autonomous underwater vehicle

Acoustic processing for source localization is often limited by uncertainties in the physical properties of the ocean environment, such as the seabed geoacoustic parameters and water-column sound-speed structure. Uncertainties in environmental parameters can be the result of measurement uncertainties and of spatial and temporal variability. Quantifying environmental uncertainties and examining how they are transfered into uncertainties for source localization represents an important and challenging problem. In this paper, Bayesian inference theory is applied to estimate the uncertainties of geoacoustic inversion in the form of marginal probability distributions for the seabed parameters. In addition, a Bayesian form of focalization is developed which incorporates uncertain environmental parameters into the source localization, and quantifies the resulting localization uncertainty in terms of the 2-D marginal probability distribution for source range and depth (i.e., a probability ambiguity surface). Localization uncertainties are examined as a function of the uncertainties in geoacoustic parameters and ocean sound-speed profile.

Matched mode processing (MMP), which introduces the acoustic propagation model in the complex underwater waveguides, has been one of the promising directions in source localization. The well-known Capon method is often used for MMP due to the capability of high resolution; however, its performance may degrade significantly in the presence of mismatch. There are various mismatches such as environmental uncertainties, array calibration errors or signal random distortions that make it quite difficulty for source localization in practice. In this paper, we develop a robust Capon MMP method based on the optimization of worst-case performance. The weight vector is obtained with a convex optimization based algorithm using the second-order cone programming (SOCP). Simulations with the benchmark cases of general mismatch show that the proposed robust Capon method for MMP provides certain robustness against mismatches as well as effective capability of sidelobe suppression.

Passive source localization in shallow water has always been an important and challenging problem. Implementing scientific research, surveying, and monitoring using a short, less than ten meter long, horizontal linear array has received considerable attention in the recent years. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, it is usually difficult to obtain a sufficient spatial gain for localizing long-range sources due to its limited physical size. To address this problem, a localization approach is proposed which is based on matched-field processing of the likelihood of the passive source localization in shallow water, as well as inter-position processing for the improved localization performance and the enhanced stability of the estimation process. The ability of the proposed approach is examined through the two-dimensional synthetic test cases which involves ocean environmental mismatch and position errors of the short array. The presented results illustrate the localization performance for various source locations at different signal- to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the short array moves at a low speed along a straight line at a certain depth.

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicle's dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard camera's field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

In this paper, a robust sliding mode control is used to control an autonomous underwater vehicle. The modeling of the autonomous underwater vehicle with structure uncertainties is conducted. Sliding mode control (SMC) has robustness property in the environmental disturbances and uncertain parameters. The asymptotic tracking performance can be verified by the Lyapunov stability. The proposed control theory is demonstrated by actual tracking simulations using the autonomous underwater vehicle system. The simulations results showed promising tracking performance for the proposed method.

A mid-size Odyssey IIb autonomous underwater vehicle (AUV) was retrofitted with the advanced vectored-thrust system presently installed on AUVs manufactured by Bluefin Robotics, Inc. Subsequent modifications to this thrust system decreased the radiated acoustic and vibration noise levels recorded by an eight-element hydrophone array mounted on the AUV’s inner shroud by 20 to 50 dB across the 20 Hz to 10 kHz band. This reduction in self-noise levels to near, or at, background ocean noise levels permits the use of the vehicle-mounted hydrophone array in passive ocean acoustic studies. One example is the application of passive synthetic aperture processing techniques to provide greater spatial resolution estimates of the direction of low frequency sources. Doppler spreading caused by medium motion is a limiting factor in array gain. At mid frequencies (1–10 kHz), the complexity of the received acoustic field created by scattering off the AUV body is partly captured in the array processing by the use of replica vectors measured in a calibration tank. These empirical replica vectors decrease the azimuthally dependent degradation in beamforming performance over that of plane waves. [Work supported by ONR, Code 321(US).]

An Odyssey IIb autonomous underwater vehicle (AUV) made by Bluefin Robotics, Inc., was acquired by the Marine Physical Laboratory, Scripps Institution of Oceanography, to conduct research in underwater acoustics as well as provide a platform for other scientific studies. The original Odyssey IIb tail cone was replaced with a ducted fan, vectored thrust system installed on vehicles currently sold by Bluefin. In initial sea tests with the new thrust system, the acoustic self noise levels of the vehicle while underway were 20 to 50 dB higher than typical ocean background noise levels, preventing the vehicle's use as a receiver of low level sounds. Controlled tests were performed to characterize the radiated and vibration noise of the AUV propulsion and actuators. Once this baseline was established, changes were made, mostly to the tail cone propulsion, to decrease the vehicle's self noise. The resulting self noise levels of the AUV from 10 Hz up to 10 kHz measured while underway by a hydrophone mounted on the AUV's inner shroud now are at or below typical shallow water background noise levels except in three bands; below 250 Hz, around 500 Hz, and from 0.9 to 2.0 kHz. The goal of this paper is to describe these changes and their effects in lowering vehicle noise levels.

Advances in Bayesian computational technology in the last decade have enabled the development of new source separation and source localization algorithms. These algorithms are greatly improved by the encoding of prior information about a specific problem in the form of the chosen relevant model parameters, the assignment of the likelihood functions, and the assignment of the prior probabilities of the model parameter values. I refer to such source separation algorithms as informed source separation for the reason that they are endowed with specific and often vital information about the problem. Furthermore, the Bayesian methodology allows source separation to be united with source localization simply by including the model parameters that are of interest to the researcher. Here, I will discuss the union of source separation and source localization under the Bayesian methodology, the incorporation of prior information, and the construction of an informed algorithm using the new computational technologies that allow us to estimate the values of the parameters that define these high‐dimensional problems.

It has been carried out modeling of the sea and ocean 3D acoustic waveguide. On the basis of Hamiltonian equations of rays it is studied a dynamics of rays. It has been shown that for acoustic waveguide in a shallow sea with non-level bottom under the rays propagation in a waveguide dependence of the of temporal frequency upon the output angle represents a fractal measure in accordance with Abdullaev-Zaslavsky result. For the ocean 3D acoustic waveguide on the basis of solving the eiconal equations in the Hamiltonian form it has been studied the fractal dynamics, including the chaotic one. There are presented the data of numerical solution of equations for the typical acoustic channel in the North-Atlantic region. The conditions for the Arnold diffusion effect realization are discussed.

This dissertation is concerned with the realities of applying matched-field processing in realistic scenarios. Matched-field processing (MFP) is a model-based source localization method which is a function of the ocean environmental parameters. When the values of the environmental parameters are imprecisely known, the performance of MFP can be severely degraded. These uncertainties may arise from imprecise measurement of the environmental parameters or from random environmental effects such as surface and internal wave motion. The environmental parameters may also vary from the source to the receiver. These effects prevent the use of a single propagation model. In this dissertation, MFP methods which are robust to environmental uncertainties are derived. Two algorithms are developed to address the problem of MFP in an uncertain environment. The first is suitable for scenarios where a small number of environmental parameters are uncertain. The Replica-Subspace Weighted Projections algorithm (RSWP) is derived as a computationally-efficient approximation to the maximum-likelihood estimator. It estimates the source location parameters jointly with the uncertain environmental parameters. The second algorithm, called the multiple uncertainty-RSWP algorithm (MU-RSWP), is suitable for use when a large number of environmental parameters are uncertain. It is based on a computationally-efficient approximation to the maximum a posteriori (MAP) estimator. Both of these methods also have interpretations in terms of vector spaces. Results are shown for both algorithms using simulated and experimental data. It is also shown that the MU-RSWP algorithm can be applied directly for robust MFP when using a short array.

Corrigendum.

Accurate modeling of acoustic propagation in the ocean waveguide is important to SONAR-performance prediction, and requires, particularly in shallow water environments, characterizing the bottom reflection loss with a precision that databank-based modeling cannot achieve. Recent advances in the technology of autonomous underwater vehicles (AUV) make it possible to envision a survey system for seabed characterization composed of a short array mounted on a small AUV. The bottom power reflection coefficient (and the related reflection loss) can be estimated passively by beamforming the naturally occurring marine ambient-noise acoustic field recorded by a vertical line array of hydrophones. However, the reduced array lengths required by small AUV deployment can hinder the process, due to the inherently poor angular resolution. In this dissertation, original data-processing techniques are presented which, by introducing into the processing chain knowledge derived from physics, can improve the performance of short arrays in this particular task. Particularly, the analysis of a model of the ambient-noise spatial coherence function leads to the development of a new proof of the result at the basis of the bottom reflection-loss estimation technique. The proof highlights some shortcomings inherent in the beamforming operation so far used in this technique. A different algorithm is then proposed, which removes the problem achieving improved performance. Furthermore, another technique is presented that uses data from higher frequencies to estimate the noise spatial coherence function at a lower frequency, for sensor spacing values beyond the physical length of the array. By "synthesizing" a longer array, the angular resolution of the bottom-loss estimate can be improved, often making use of data at frequencies above the array design frequency, otherwise not utilized for beamforming. The proposed algorithms are demonstrated both in simulation and on real data acquired during several experimental campaigns.

We propose time-frequency methods to filter dispersive guided wave signals. Guided waves occur in acoustical propagation (oceanic waveguides), geophysics (layered medium), or optics (dielectric optical waveguides). In waveguides, signals can be decomposed into normal modes which contain information on environmental parameters and source localization. As modes present nonlinear time-frequency evolution, modal filtering is not possible with conventional tools. To overcome the difficulty presented by these nonlinearties, we have developed matched tools: matched frequency and time-frequency representations and the modal filterings associated with these representations. The tools developed are based on unitary equivalence principle. Performance and robustness of different proposed modal filters are evaluated and compared. All of these tools can be used for both source localization and environmental inversion.

Source localization using passive sensor arrays has been an active research problem for many years. Most near-field source localization algorithms involve two separate estimations, namely, relative time-delay estimations and source location estimation. A one-step maximum-likelihood parametric source localization algorithm is proposed based on the maximum correlation between time shifted sensor data at the true source location. The performance of the algorithm is evaluated and shown to approach the Cramer-Rao bound asymptotically in simulations.

Transmission lines are vital components in power systems. Outages of transmission lines caused by over-temperature is a major threat to system reliability, so it is necessary to efficiently simulate line temperature under both normal operation conditions and foreseen fault conditions. Existing methods based on thermal-steady-state analyses cannot support the simulation of transient temperature evolution, and thus cannot provide timing information needed for taking remedial actions. Moreover, conventional numerical method requires huge computational efforts and barricades system-wide analysis. In this regard, this paper derives an approximate analytical solution of transmission-line temperature evolution enabling efficient analysis on multiple operation states. Considering the uncertainties in environmental parameters, the region of over-temperature is constructed in the environmental parameter space to realize the over-temperature risk assessment in both the planning stage and real-time operations. A test on a typical conductor model verifies the accuracy of the approximate analytical solution. Based on the analytical solution and numerical weather prediction (NWP) data, an efficient simulation method for temperature evolution of transmission systems under multiple operation states is proposed. As demonstrated on a Northeast Power Coordinating Council (NPCC) 140-bus system, it achieves over 1000 times of efficiency enhancement, verifying its potential in online risk assessment and decision support.