Sensor bias estimation from measurements of known trajectories
Paul D. Burns, William Dale Blair · 2005
In this paper, we consider the problem of estimating sensor biases (e.g., range and bearing biases) from measurements of targets flying on known trajectories (i.e., zero, or near-zero, process noise). The key difficulty with this form of sensor registration is that the a priori kinematic uncertainty of the target state is often inaccurate. In this paper, we examine the sensitivity of two bias estimators: the extended Kalman filter (EKF) and nonlinear least squares (NLS) estimator, to the precision of the a priori information about the object. We examine a simplified two-dimensional problem in order to simplify the calculations required in the NLS iteration. In addition, the performance of the estimators is compared with the derived Cramer-Rao lower bound (CRLB).