Parameter identification for inflight detection and characterization of aircraft icing

Abstract

Increasing interest in aircraft icing has motivated the proposal of a new ice management system that would provide inflight monitoring of ice accretion effects. Since these effects are manifested in the flight dynamics, parameter identification is a critical element of ice detection. In particular, identification must provide timely and accurate parameter estimates under normal operational input in the presence of disturbances and measurement noise. This paper evaluates a batch least-squares algorithm, an extended Kalman filter, and an H ∞ algorithm in the context of icing detection. Simulation results show that only the H ∞ method provides a timely and accurate icing indication.