OPTIMAL RECURSIVE ITERATIVE ALGORITHM FOR DISCRETE NONLINEAR LEAST-SQUARES ESTIMATION

The estimation algorithm developed offers an alternative to standard r ecursive nonlinear estimators such as the extended Kalman filter and t he iterated extended Kalman filter. The algorithm, which is developed from a quadratic cost function basis, splits the problem of cost funct ion minimization into a linear first step and a nonlinear second step by defining new first-step states that are nonlinear combinations of t he unknown states. Estimates of the first-step states are obtained by minimizing the first-step cost function using a Kalman filter formulat ion. Estimates of the unknown, or second-step, states are obtained by minimizing the second-step cost function using an iterative Gauss-Newt on algorithm. The two-step estimator is shown to be optimal for static problems in which the time variation of the measurement equation can be separated from the unknowns. This method is then generalized by app roximating the nonlinearity as a perturbation of the dynamic update, w hile keeping the measurement cost function the same, In contrast, the extended Kalman filter and the iterated extended Kalman filter lineari ze the measurement cost function, resulting in suboptimal estimates. T wo example applications confirm these analytical results.