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oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features

Methods in oceanography 2014年 10卷 21-43页

作者： Reed, Brooks Hover, Franz Massachusetts Institute of Technology Department of Mechanical Engineering Cambridge 02139 MA United States MIT-WHOI Joint Program Woods Hole Oceanographic Institution Woods Hole MA United States

We present an integrated framework for joint estimation and pursuit of dynamic features in the ocean, over large spatial scales and with multiple collaborating vehicles relying on limited communications. Our approach uses ocean model predictions to design closed-loop networked control at short time scales, and the primary innovation is to represent model uncertainty via a projection of ensemble forecasts into local linearized vehicle coordinates. Based on this projection, we identify a stochastic linear time-invariant model for estimation and control design. The methodology accurately decomposes spatial and temporal variations, exploits coupling between sites along the feature, and allows for advanced methods in communication-constrained control. Simulations with three example datasets successfully demonstrate the proof-of-concept. © 2014 The Authors.

关键词： Autonomous underwater vehicles Collaborative control Ensemble forecasts Feature tracking Linearization System identification

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APPROXIMATE LEAST SQUARES PARAMETER ESTIMATION WITH STRUCTURED OBSERVATIONS

APPROXIMATE LEAST SQUARES PARAMETER ESTIMATION WITH STRUCTUR...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Atulya Yellepeddi James C. Preisig Electrical Eng./Applied Ocean Physics and Eng. MIT-WHOI Joint Program Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution

ISBN: (纸本)9781479928941

The solution of inverse problems where the parameter being estimated has a known structure has been widely studied. In this work, we consider the situation where it is not appropriate to assume a structure for the parameter, but the observations on which the estimate are based are structured;specifically, when the observations are parametrized by a decomposable graphical model. This translates to structured sparsity of the inverse covariance matrix for Gaussian distributed observation vectors. We present an approximate least squares method which takes advantage of the structure to reduce the complexity of least squares. The approximate least squares method can be implemented recursively for even lower complexity. It is shown that the proposed method is asymptotically equivalent to least squares parameter estimation for a large number of observations. The properties of the algorithm are verified by simulation.

关键词： Least squares method variance covariance matrix Asymptotically equivalent Inverse method Parameter estimation Approximate

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Tracking Ocean Fronts with Multiple Vehicles and Mixed Communication Losses

Tracking Ocean Fronts with Multiple Vehicles and Mixed Commu...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Brooks L. Reed Franz S. Hover the MIT/WHOI Joint Program in Oceanographic Engineering MIT Department of Mechanical Engineering

ISBN: (纸本)9781467363563

We make two contributions toward integrated monitoring over large spatial scales, with multiple collaborating vehicles. Our focus is dynamic ocean features such as fronts and plumes. To support strong networked-control designs, we first develop a clean linear time-invariant framework for tracking features, that directly couples the global structure of the process to vehicle positioning. To address the packet loss inherent in underwater acoustic communications, we then extend the synthesis technique of Imer et al. [1] to the case where measurements and control commands suffer loss with differing statistics among the multiple channels. Simulations show that the integrated feedback system achieves good performance in front tracking.

关键词： Tracking Ocean Fronts Multiple Vehicles Mixed Communication Losses

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DESIGN OF DECISION DEVICE FOR THE ADAPTATION OF DECISION DIRECTED EQUALIZERS

DESIGN OF DECISION DEVICE FOR THE ADAPTATION OF DECISION DIR...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Atulya Yellepeddi James C. Preisig Electrical Eng./Applied Ocean Physics and Eng. MIT-WHOI Joint Program Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution

ISBN: (纸本)9781479903573

Adaptation of the coefficients of an equalizer usually takes place in the decision directed mode. The decision used for adaptation has a profound influence on the performance of the equalizer. In this work, we consider the influence of the probabilistic model used by a soft information decision device on the performance of the equalizer. It is shown that the equalizer output is distributed as a Gaussian Mixture, and these statistics are used to form a soft-decision device. We show that this is very near optimal in the sense of the Recursive Expected Least Squares algorithm introduced in prior work. Sub-optimal decision devices that are often more practical are also introduced and the loss of performance as a result of using these is considered.

关键词： Equalizers adaptive signal processing adaptive equalizers time-varying channels

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Robust Minimum Energy Wireless Routing for Underwater Acoustic Communication Networks

Robust Minimum Energy Wireless Routing for Underwater Acoust...

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Institute of Electrical and Electronics Engineers Globecom Workshops

作者： Brooks L. Reed Milica Stojanovic Urbashi mitra Franz S. Hover MIT/WHOI Joint Program in Oceanographic Engineering Northeastern University Department of Electrical Engineering University of Southern California Department of Electrical Engineering and Computer Science MIT Department of Mechanical Engineering

ISBN: (纸本)9781467349420

Marine robots are an increasingly attractive means for observing and monitoring the ocean, but underwater acoustic communications remain a major challenge. The channel exhibits long delay spreads with frequency-dependent attenuation;moreover, it is time-varying. We consider the minimum energy wireless transmission problem [MET], augmented by the practical condition that constraints on link power must be satisfied in probability. For this, we formulate the robust counterpart of the multicommodity mixed-integer linear programming (MDLP) model from Haugland and Yuan [1], and derive scaled power levels that account for uncertainty. Our main result is that the deterministic formulation with these scaled power levels recovers exactly the optimal robust solution in the absence of correlations, and therefore allows for efficient solution via MILP. This approach achieves significant power improvements over heuristics, and naturally lends itself to vehicle networks.

关键词： underwater acoustic communication Energy sources networks(communications) wireless power level Power (Psychology) mobile data link protocol Heuristics Vehicle Network Network routing

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Soft-adaptive turbo equalization: Using soft information in adaptation

Soft-adaptive turbo equalization: Using soft information in ...

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Asilomar Conference on Signals, Systems & Computers

作者： Atulya Yellepeddi James C. Preisig Electrical Engineering/Applied Ocean Science and Engineering WHOI Joint Program MIT USA Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

ISBN: (纸本)9781467350501

Turbo equalizers have been exploited for underwater wireless communication due to their excellent practical performance. As the equalizer is adaptive, it is possible to take advantage of the soft information available from the decoder in the adaptation process. In this paper, we consider a method to do this and compare it with the use of soft information in feedback, which is a method that has been considered in the past. Data from the KAM11 experiment demonstrates that using soft information in adaptation can improve performance over using it only in feedback. We also consider combining these different approaches and how this affects performance.

关键词： Using how this soft information Equalizers Worm gears Turbo CARRIER SYSTEM Feedback Equalization decoders

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Exactly sparse delayed-state filters

Exactly sparse delayed-state filters

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2005 IEEE International Conference on Robotics and Automation

作者： Eustice, Ryan M. Singh, Hanumant Leonard, John J. MIT/WHOI Joint Program in Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Ocean Engineering Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)078038914X

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the "full-covariance" solution. ©2005 IEEE.

关键词： Digital filters

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Improved vehicle based multibeam bathymetry using sub-maps and SLAM

Improved vehicle based multibeam bathymetry using sub-maps a...

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2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： C. Roman H. Singh MIT/WHOI Joint Program in Applied Ocean Science and Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

This paper presents an algorithm to improve sub-sea acoustic multibeam bottom mapping based on the simultaneous mapping and localization (SLAM) methodology. Multibeam bathymetry from underwater water vehicles can yield valuable large scale terrain maps of the sea door, but the overall accuracy of these maps is typically limited by the accuracy of the vehicle position estimates. The solution presented here uses small bathymetric patches created over short time scales in a sub-mapping context. These patches are registered with respect to one another and assembled in a single coordinate frame to produce a more accurate terrain estimate and provide improved renavigation of the vehicle trajectory. The mapping is implemented using a delayed state extended Kalman filter (EKF) and results are shown for a real world multibeam data set collected at the mid-Atlantic ridge using the JASON ROV.

关键词： Simultaneous localization and mapping Terrain mapping Marine vehicles Underwater vehicles Underwater acoustics Large-scale systems Yield estimation Assembly Delay Remotely operated vehicles

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Exactly Sparse Delayed-State Filters

Exactly Sparse Delayed-State Filters

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IEEE International Conference on Robotics and Automation (ICRA)

作者： R.M. Eustice H. Singh J.J. Leonard MIT/WHOI Joint Program in Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA Department of Ocean Engineering Massachusetts Institute of Technology Cambridge MA USA

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature based SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the “full-covariance” solution.

关键词： Delay Simultaneous localization and mapping Navigation Cameras Oceans Information filters Sparse matrices Information filtering Remotely operated vehicles Robot vision systems

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A technique for generating synthetic acoustic fields in shallow water

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The Journal of the Acoustical Society of America 2005年第S1期76卷 S11-S11页

作者： Michael S. Wengrovitz George V. Frisk MIT/WHOI Joint Program in Oceanography/Oceanographic Engineering Woods Hole MA 02543 Woods Hole Oceanographic Institution Woods Hole MA 02543

A numerical scheme for generating both the trapped mode and continuum portions of acoustic fields for a horizontally stratified ocean and bottom is presented. The technique is based on the fact that the branch line integral corresponding to the continuum portion of the field can be performed by Hankel transforming a modified Green's function. The modified Green's function is obtained by removing the pole contributions from the actual Green's function. The continuum portion is then added to the trapped mode portion to form the total synthetic field. The technique has the advantage that it is fast, accurate, and can be used for a more general geoacoustic model than the Pekeris waveguide. Synthetic results for several examples are presented and discussed.

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