GaussNewton filtering incorporating LevenbergMarquardt methods for tracking
This paper shows that the LevenbergMarquardt algorithms (LMA) can be merged into the GaussNewton filters (GNF) to track difficult, non-linear trajectories, with improved convergence. The GNF discussed first in this paper is an iterative filter, with memory that was introduced by Norman Morrison (1969) [1]. To improve the computation demands of the GNF, we adapted the GNF to a recursive version. The original GNF uses back propagation of the predicted state to compute the Jacobian matrix over the filter memory length. The LMA are optimisation techniques widely used for data fitting (Marquardt, 1963 [2]). These optimisation techniques are iterative and guarantee local convergence.
gaussnewton filter levenbergmarquardt tracking