On the identification of stochastic biases in linear time invariant systems
Thomas A. Chmielewski, Paul R. Kalata · 2005
This paper addresses the existence of bias estimators. An approach to bias estimation is to augment the system state with bias states and implement a Kalman filter. Computational advantage can be gained using two parallel, reduced order Kalman filters. Conditions for existence of bias estimators for a linear, time invariant system with unknown, constant state and measurement biases are derived. A reduced row observability test matrix is used to show a necessary and sufficient condition for which complete bias observability does not exist. Examples are presented.