Distributed Adaptive Estimation with ISS Guarantees for Sensor Networks with Partially Unknown Source Dynamics

TL;DR

This paper introduces distributed adaptive estimation methods for sensor networks with unknown source dynamics, providing ISS guarantees, robustness, and scalability demonstrated through simulations.

Contribution

It develops both continuous-time and discrete-time adaptive observer designs with ISS guarantees, handling model uncertainty and sampling effects in sensor networks.

Findings

Proves stability and convergence of estimates despite uncertainties.

Derives ISS bounds showing robustness to process noise.

Simulations confirm accurate tracking and scalability.

Abstract

This paper studies distributed adaptive estimation over sensor networks with partially unknown 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's 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.