Distributed Adaptive Estimation over Sensor Networks with Partially Unknown Source Dynamics

2025-11-10

AI Summary

This paper addresses the problem of distributed adaptive estimation in sensor networks under partial source dynamics uncertainty. For directed communication topologies, a unified continuous- and discrete-time adaptive observer framework is proposed: the discrete-time scheme incorporates constant gains and stepwise regression normalization to mitigate sampling-induced distortions. By designing graph-dependent coupling operators and employing input-to-state-stable (ISS)-type Lyapunov analysis, a synergistic mechanism is established between local parameter estimation and inter-node information exchange. Theoretical analysis guarantees uniform boundedness of all internal signals and asymptotic consensus of node-wise estimates to the true source signal, demonstrating strong robustness against model uncertainties and process noise. Numerical simulations validate the method’s accuracy, convergence rate, and scalability across star, ring, and path topologies.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Graphical Models
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper studies distributed adaptive estimation over sensor networks with partially known source dynamics. We present parallel continuous-time and discrete-time designs in which each node runs a local adaptive observer and exchanges information over a directed graph. For both time scales, we establish stability of the network coupling operators, prove boundedness of all internal signals, and show convergence of each node estimate to the source despite model uncertainty and disturbances. We further derive input-to-state stability (ISS) bounds that quantify robustness to bounded process noise. A key distinction is that the discrete-time design uses constant adaptive gains and per-step regressor normalization to handle sampling effects, whereas the continuous-time design does not. A unified Lyapunov framework links local observer dynamics with graph topology. Simulations on star, cyclic, and path networks corroborate the analysis, demonstrating accurate tracking, robustness, and scalability with the number of sensing nodes.

Problem

• Distributed adaptive estimation with partially unknown source dynamics
• Stability and convergence under model uncertainty and disturbances
• Robustness analysis via input-to-state stability bounds

Innovation

• Distributed adaptive observers on directed graphs
• Unified Lyapunov framework linking local dynamics with topology
• Constant adaptive gains with regressor normalization in discrete-time