A new likelihood approach to autonomous multiple model estimation.

This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure.

Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.

In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions.