
Accession Number : AD0730688
Title : State Estimation with Small NonLinearities.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CALIF DEPT OF AERONAUTICS AND ASTRONAUTICS
Personal Author(s) : Conrad,Bjoern
Report Date : MAR 1971
Pagination or Media Count : 93
Abstract : A variety of techniques are available for estimating the states of nonlinear dynamic systems from noisy data. These procedures are generally equivalent when applied to linear systems. This report investigates the difference between several of these procedures in the presence of small dynamic and observational nonlinearities. Four discrete estimation algorithms are analyzed. The first is a strictly least square estimator, while the other three are recursive algorithms similar to the Kalman filter used for estimating the states of linear systems. The product of this research is a group of analytic expressions for the mean and covariance of the error in each of those estimators so that they may be compared without lengthy MonteCarlo simulations. The covariance expressions show that, to first order, all the estimators have the same covariance. Expressions for the means, however, show that each estimator has a different bias. Several examples are carried out demonstrating that the relative magnitudes of the bias errors in the various estimators can be a strong function of such parameters as initial covariances and number of data points being considered. In fact, under some circumstances it appears that more complicated (seemingly superior) algorithms can have larger biases than smaller ones. (Author)
Descriptors : (*INFORMATION THEORY, NONLINEAR SYSTEMS), (*CONTROL SYSTEMS, MATHEMATICAL MODELS), DIFFERENCE EQUATIONS, PARTIAL DIFFERENTIAL EQUATIONS, LEAST SQUARES METHOD, MONTE CARLO METHOD, MULTIVARIATE ANALYSIS, LINEAR SYSTEMS, ALGORITHMS
Subject Categories : Statistics and Probability
Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE