Explore analytical and empirical tools for nonlinear network observability in autonomous systems in this 44-minute lecture from the Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 workshop. Delve into the intersection of geometric nonlinear systems theory and empirical Gramian methods for analyzing engineered and biological multiagent systems. Learn about optimal sensor placement, network observability, and the impact of process noise on stochastic systems. Discover applications in autonomous multiagent systems, network synthesis with privacy guarantees, disease spread tracking, and insect wing strain sensor placement. Gain insights into nonlinear dynamics, remote sensing, robustness, and active sensing in engineered systems. Examine topics such as gyroscopic sensing, reduced-order modeling, Fisher information bounds, and optimization algorithms for sensor selection and placement.
Analytical and Empirical Tools for Nonlinear Network Observability in Autonomous Systems
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Intro
Nonlinear Dynamics and Control Lab
Remote Sensing
Dynamics, Control, Sensing, Robustness
Agility and localization in biological systems
Active sensing in engineered systems: Wind-finding
Gyroscopic sensing in insect wings
Reduced-order modeling
Nonlinear observability
Observability via linearization about trajectory
Empirical observability Gramian
Limit case
Finite epsilon case
Fisher information bound
Sensor Selection - Problem framework
Sensor placement results
Optimal sensor placement
Network Observability
Optimization Algorithm
Virus Spreading Model (SIS)
Sparse or Dense Network Node Sensor Selection
Privacy in Networked Systems
Network Security
Mathematical Modeling
Optimal sensor locations for vortex sensing
Range-only and bearing-only navigation
Ongoing work
Acknowledgements
Taught by
Institute for Pure & Applied Mathematics (IPAM)
