An algorithm for maximum likelihood estimation using an efficient method for approximating sensitivities

TLDR: An algorithm for maximum likelihood (ML) estimation is developed primarily for multivariable dynamic systems based on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES), which eliminates the need to derive sensitivity equations.

Abstract: An algorithm for maximum likelihood (ML) estimation is developed primarily for multivariable dynamic systems. The algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). The method determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort compared with integrating the analytically determined sensitivity equations or using a finite-difference method. Different surface-fitting methods are discussed and demonstrated. Aircraft estimation problems are solved by using both simulated and real-flight data to compare MNRES with commonly used methods; in these solutions MNRES is found to be equally accurate and substantially faster. MNRES eliminates the need to derive sensitivity equations, thus producing a more generally applicable algorithm.