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OCEANS 2024 - Halifax, OCEANS 2024

作者： Srinivasan, Margaret Tsontos, Vardis California Institute of Technology Applied Science System Engineering Jet Propulsion Laboratory PasadenaCA United States

ISBN: (纸本)9798331540081

A continuous global satellite radar altimetry data time series spanning 32 years has made significant advances in research and operational applications possible. The TOPEX/Poseidon satellite mission, launched in 1992, was intended to test technology and data systems to precisely measure height variations across the ocean-'ocean surface topography' (OST) -on a global scale from space. The mission performed so successfully that it was followed by the 'Jason-series' satellites;Jason 1, Jason 2, Jason 3, and the newest, Sentinel 6 Michael Freilich, with more satellites in the series in preparation. These U.S.-European collaborations have significantly enhanced understanding of ocean and coastal dynamics and have made large-scale reservoir and lake observations since 1992. The 2022 launch of the Surface Water and Ocean Topography (SWOT) mission has further advanced knowledge of ocean dynamics and extends observations to high-resolution land surface water observations. OST data have been crucial in monitoring global sea level rise, understanding ocean circulation and heat content, and supporting coastal impact assessments and climate modeling. Beyond research, OST observations benefit operational oceanographic and water resource organizations and weather forecasting agencies worldwide. They inform shipping, fishery and marine operations, and decision-making and disaster management in coastal zones. The continuous OST record aids in analyzing trends, tracking sea-level rise, assessing ocean health, and supporting climate change mitigation strategies. Partnerships between NASA, the French space agency, Centre National d'Etudes Spatiales (CNES), NOAA, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Space Agency (ESA), and the European meteorological and EUMETSAT have been instrumental in developing an essential climate data record, providing substantial value to both research and operational communities. This talk, summarizing findings

关键词： NASA

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Satellite Altimetry for Ocean and Coastal Applications: Promoting and Supporting Applications User Communities

Satellite Altimetry for Ocean and Coastal Applications: Prom...

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OCEANS

作者： Margaret Srinivasan Vardis Tsontos Applied Science System Engineering Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

A continuous global satellite radar altimetry data time series spanning 32 years has made significant advances in research and operational applications possible. The TOPEX/Poseidon satellite mission, launched in 1992, was intended to test technology and data systems to precisely measure height variations across the ocean-“ocean surface topography” (OST) -on a global scale from space. The mission performed so successfully that it was followed by the “Jason-series” satellites; Jason 1, Jason 2, Jason 3, and the newest, Sentinel 6 Michael Freilich, with more satellites in the series in preparation. These U.S.-European collaborations have significantly enhanced understanding of ocean and coastal dynamics and have made large-scale reservoir and lake observations since 1992. The 2022 launch of the Surface Water and Ocean Topography (SWOT) mission has further advanced knowledge of ocean dynamics and extends observations to high-resolution land surface water observations. OST data have been crucial in monitoring global sea level rise, understanding ocean circulation and heat content, and supporting coastal impact assessments and climate modeling. Beyond research, OST observations benefit operational oceanographic and water resource organizations and weather forecasting agencies worldwide. They inform shipping, fishery and marine operations, and decision-making and disaster management in coastal zones. The continuous OST record aids in analyzing trends, tracking sea-level rise, assessing ocean health, and supporting climate change mitigation strategies. Partnerships between NASA, the French space agency, Centre National d'Etudes Spatiales (CNES), NOAA, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Space Agency (ESA), and the European meteorological and EUMETSAT have been instrumental in developing an essential climate data record, providing substantial value to both research and operational communities. This talk, summarizing findings

关键词： Sea surface Satellites Time series analysis Sea measurements Land surface Europe Weather forecasting Aerospace electronics Surface topography Altimetry

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Robust Optimal Network Topology Switching for Zero Dynamics Attacks 63

Robust Optimal Network Topology Switching for Zero Dynamics ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tsukamoto, Hiroyasu Ibrahim, Joshua D. Hajar, Joudi Ragan, James Chung, Soon-Jo Hadaegh, Fred Y. University of Illinois at UrbanaChampaign Department of Aerospace Engineering UrbanaIL United States Jet Propulsion Laboratory PasadenaCA United States Division of Engineering and Applied Science Caltech PasadenaCA United States

ISBN: (纸本)9798350316339

The intrinsic, sampling, and enforced zero dynamics attacks (ZDAs) are among the most detrimental stealthy attacks in robotics, aerospace, and cyber-physical systems. They exploit internal dynamics, discretization, redundancy/asynchronous actuation and sensing, to construct disruptive attacks that are completely stealthy in the measurement. They work even when the systems are both controllable and observable. This paper presents a novel framework to robustly and optimally detect and mitigate ZDAs for networked linear control systems. We utilize controllability, observability, robustness, and sensitivity metrics written explicitly in terms of the system topology, thereby proposing a robust and optimal switching topology formulation for resilient ZDA detection and mitigation. Our main contribution is the reformulation of this problem into an equivalent rank-constrained optimization problem (i.e., optimization with a convex objective function subject to convex constraints and rank constraints), which can be solved using convex rank minimization approaches. The effectiveness of our method is demonstrated using networked double integrators subject to ZDAs. © 2024 IEEE.

关键词： Observability

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Regret-Optimal Defense Against Stealthy Adversaries: A system Level Approach 63

Regret-Optimal Defense Against Stealthy Adversaries: A Syste...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tsukamoto, Hiroyasu Hajar, Joudi Chung, Soon-Jo Hadaegh, Fred Y. University of Illinois at Urbana-Champaign Department of Aerospace Engineering UrbanaIL United States Jet Propulsion Laboratory PasadenaCA United States Caltech Division of Engineering and Applied Science PasadenaCA United States

ISBN: (纸本)9798350316339

Modern control designs in robotics, aerospace, and cyber-physical systems rely heavily on real-world data obtained through system outputs. However, these outputs can be compromised by system faults and malicious attacks, distorting critical system information needed for secure and reliable operation. In this paper, we introduce a novel regret-optimal control framework for designing controllers that make a linear system robust against stealthy attacks, including both sensor and actuator attacks. Specifically, we present (a) a convex optimization-based system metric to quantify the regret under the worst-case stealthy attack (the difference between actual performance and optimal performance with hindsight of the attack), which adapts and improves upon the H2 and H∞ norms in the presence of stealthy adversaries, (b) an optimization problem for minimizing the regret of (a) in system-level parameterization, enabling localized and distributed implementation in large-scale systems, and (c) a rank-constrained optimization problem equivalent to the optimization of (b), which can be solved using convex rank minimization methods. We also present numerical simulations that demonstrate the effectiveness of our proposed framework. © 2024 IEEE.

关键词： Large scale systems

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Robust Optimal Network Topology Switching for Zero Dynamics Attacks

Robust Optimal Network Topology Switching for Zero Dynamics ...

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IEEE Conference on Decision and Control

作者： Hiroyasu Tsukamoto Joshua D. Ibrahim Joudi Hajar James Ragan Soon-Jo Chung Fred Y. Hadaegh Department of Aerospace Engineering University of Illinois at UrbanaChampaign Urbana IL Jet Propulsion Laboratory Pasadena CA Division of Engineering and Applied Science Caltech Pasadena CA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Sensitivity Network topology Switches Controllability Aerodynamics Robot sensing systems Minimization Topology Observability Optimization

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Regret-Optimal Defense Against Stealthy Adversaries: A system Level Approach

Regret-Optimal Defense Against Stealthy Adversaries: A Syste...

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IEEE Conference on Decision and Control

作者： Hiroyasu Tsukamoto Joudi Hajar Soon-Jo Chung Fred Y. Hadaegh Department of Aerospace Engineering University of Illinois at Urbana-Champaign Urbana IL Jet Propulsion Laboratory Pasadena CA Division of Engineering and Applied Science Caltech Pasadena CA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Modern control designs in robotics, aerospace, and cyber-physical systems rely heavily on real-world data obtained through system outputs. However, these outputs can be compromised by system faults and malicious attacks, distorting critical system information needed for secure and reliable operation. In this paper, we introduce a novel regret-optimal control framework for designing controllers that make a linear system robust against stealthy attacks, including both sensor and actuator attacks. Specifically, we present (a) a convex optimization-based system metric to quantify the regret under the worst-case stealthy attack (the difference between actual performance and optimal performance with hindsight of the attack), which adapts and improves upon the ${\mathcal{H}}_system$ and ${\mathcal{H}}_{\infty}$ norms in the presence of stealthy adversaries, (b) an optimization problem for minimizing the regret of (a) in system-level parameterization, enabling localized and distributed implementation in large-scale systems, and (c) a rank-constrained optimization problem equivalent to the optimization of (b), which can be solved using convex rank minimization methods. We also present numerical simulations that demonstrate the effectiveness of our proposed framework.

关键词： Linear systems Measurement Minimization methods Nonlinear distortion Cyber-physical systems Robot sensing systems Numerical simulation Large-scale systems Reliability Optimization

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Using solar-viewing FTS observations to inform spectroscopic parameters

Using solar-viewing FTS observations to inform spectroscopic...

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2023 Fourier Transform Spectroscopy, FTS 2023 in Optica Sensing Congress - Part of Optical Sensors and Sensing Congress 2023

作者： Laughner, Joshua L. Toon, Geoffrey C. Roehl, Coleen M. Wunch, Debra Wennberg, Paul Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA United States Department of Physics University of Toronto Toronto Canada Division of Engineering and Applied Science California Institute of Technology PasadenaCA United States

Accurate information on molecular absorption line parameters is crucial to accurately interpret remotely sensed observations of atmospheric composition. We show how ground-based, solar-viewing FTS data can identify an... 详细信息

ISBN: (纸本)9781957171241

关键词： Atmospheric composition

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product

arXiv

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arXiv 2025年

作者： Hardiman-Mostow, Harris Marshak, Charles Handwerger, Alexander L. Department of Mathematics University of California - Los Angeles Los AngelesCA90095 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory California Institute of Technology United States Joint Institute for Regional Earth System Science and Engineering University of California Los Angeles United States

Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent timeseries images of the ground regardless of weather or illumination conditions. Despite SAR’s potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA’s Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model’s capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist. Copyright © 2025, The Authors. All rights reserved.

关键词： Labeled data

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Regret-Optimal Defense Against Stealthy Adversaries: A system Level Approach

arXiv

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arXiv 2024年

作者： Tsukamoto, Hiroyasu Hajar, Joudi Chung, Soon-Jo Hadaegh, Fred Y. Department of Aerospace Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Division of Engineering and Applied Science Caltech PasadenaCA United States Jet Propulsion Laboratory PasadenaCA United States

关键词： Large scale systems

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Robust Optimal Network Topology Switching for Zero Dynamics Attacks

arXiv

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arXiv 2024年

作者： Tsukamoto, Hiroyasu Ibrahim, Joshua D. Hajar, Joudi Ragan, James Chung, Soon-Jo Hadaegh, Fred Y. Department of Aerospace Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Division of Engineering and Applied Science Caltech PasadenaCA United States Jet Propulsion Laboratory PasadenaCA United States

关键词： Observability

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