High-Probability ISS Tubes for Continuous-Time State Estimation
Provides a probabilistic interpretation of ISS bounds for state estimation, offering a computationally light uncertainty quantification method for control and estimation practitioners.
The paper shows that deterministic ISS bounds for continuous-time estimation-error dynamics yield high-probability error tubes when disturbances satisfy a probabilistic envelope, and provides constructive sufficient conditions via quadratic Lyapunov inequalities. The approach is demonstrated on a positive compartment model, where ISS tubes offer a conservative but computationally cheap uncertainty baseline compared to Kalman-Bucy and moving-horizon estimators.
This paper studies a probabilistic interpretation of input-to-state stability (ISS) bounds for estimation-error dynamics in continuous-time systems. We show that, if the aggregated disturbance satisfies a probabilistic envelope in an essential-supremum sense, then deterministic ISS bounds immediately induce high-probability error tubes. To make this interpretation constructive, we also provide explicit sufficient conditions based on quadratic Lyapunov inequalities and specialize them to positive and cooperative systems. The approach is illustrated on a positive compartment model with aggregated measurements, where ISS tubes are compared with nominal uncertainty bands produced by a Kalman--Bucy filter and by Gaussian and robust moving-horizon estimators. The examples show that ISS tubes provide a conservative but computationally light uncertainty baseline, while robust MHE is less sensitive to outlier contamination than Gaussian-based