The Kalman filter (KF) is the optimal estimator that minimizes the mean square error when
the state and measurement dynamics are linear in nature, provided the process and …

The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state
estimation with both additive and nonadditive noise structures are presented and compared …

This article presents a loosely coupled visual/inertial hybrid system for relative attitude
determination on a moving platform. The inertial measurements are accomplished by two …

This paper provides a survey of modern nonlinear filtering methods for attitude estimation.
Early applications relied mostly on the extended Kalman filter for attitude estimation. Since …

This paper presents a novel attitude estimator called the multiplicative exogenous Kalman
filter. The estimator inherits the stability properties of a nonlinear observer and the near …

Redundant attitude representations are often used in Kalman filters for estimating dynamic
states that include an attitude. A minimal three-element attitude deviation is combined with a …

The principle of the iterated extended Kalman filter has been generalized to create a new
filter that has superior performance when the estimation problem contains severe …

AN UNDERLYING assumption of the Kalman filter is that the measurement and process
disturbances can be accurately modeled as random white noise. Various mitigation …

The unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good
estimation results for estimation problems of nonlinear systems even when high nonlinearity …

TO SAVE the manual efforts and costs spent on the ground station system, and to enhance
the survivability of vehicles, autonomous systems including guidance, navigation, and …