Multiple Model Adaptive Estimation Algorithm for Systems with Parameter Change

This paper presents an autonomous multiple model (AMM) estimation algorithm for systems with sudden parameter changes. Estimates of a bank of Kalman filters (KFs) are merged based on a newly defined likelihood function. The function is composed of two measures, one for weighting the filters during the steady state mode and the other for weighting when there is a change in the parameters. Compared to the interacting multiple model (IMM) method, the KFs do not interact but compete on the basis of the likelihood function without any necessity for the mixing step. The proposed algorithm is demonstrated for estimating the residual magnetic moment (RMM) affecting the spacecraft dynamics. The numerical results show that the Competing AMM (CAMM) guarantees higher steady state accuracy than the IMM with a simpler algorithm structure.