Distributed Adaptive Estimation with ISS Guarantees for Sensor Networks with Partially Unknown Source Dynamics
For sensor network applications requiring robust distributed estimation of unknown dynamic sources, this work provides theoretical guarantees (ISS) for both continuous-time and discrete-time designs.
This paper presents distributed adaptive estimation algorithms for sensor networks tracking a source with partially unknown dynamics, achieving convergence and input-to-state stability guarantees despite model uncertainty and disturbances. Simulations on various network topologies demonstrate accurate tracking and scalability.
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.