New method for dynamic bias estimation: Gaussian mean shift registration
Zhongliang Jing · Optical Engineering · 2008
A novel algorithm, Gaussian mean shift registration (GMSR), is proposed for multisensor dynamic bias estimation. The sufficient condition for convergence of a Gaussian mean shift procedure is given, which extends the current theorem from a strictly convex kernel to a piece-wise convex and concave kernel. The Gaussian mean shift algorithm combined with the extended Kalman filter (EKF) is implemented to estimate the dynamic bias based on the measurements from a single target, which is an iterative optimization procedure. Monte Carlo simulations show that the new algorithm has significant improvement in performance with reducing root mean square (RMS) errors compared with the minimum mean square error (MMSE) estimator, based on multiple targets and multiple frames. The proposed estimator is close to the theoretical lower bound, i.e., it is more efficient in estimating the dynamic bias than other methods.