Event-triggered parameter estimator for sensor fusion
For sensor fusion systems, this work provides a theoretical guarantee of exponential convergence with event-triggered communication, addressing the need for efficient data transmission.
The paper introduces a regressor-driven event-triggered parameter estimator for sensor fusion that achieves global exponential convergence under persistent excitation, with simulations showing substantial communication savings.
This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.