Nonlinear Parameter Estimation With Segmented Data: Trajectory Estimation with Biased Measurements
A.G. Lindgren, M.L. Graham, Kai F. Gong · 2005
The problem of extracting parameters from a sequence of data generated by a nonlinear process is examined. The asymptotic normality of the posterior distribution provides a simplified and unified Bayesian and maximum likelihood approach to nonlinear estimation with segmented data. proposed method consists of locally processing data segments to produce a reduced data set. Nonlinear estimation techniques are then applied to combine these local estimates to obtain the desired overall or global estimate. reduced data set results in both computational efficiency and flexible algorithm design. illustrative example, an estimator is developed for a trajectory estimation problem where the data consists of noisy and biased angle-ofarrival measurements. The