
Accession Number : ADA298914
Title : Office of Naval Research/E21H28.
Descriptive Note : Semiannual rept. 1 Jan30 Jun 94,
Corporate Author : OFFICE OF NAVAL RESEARCH ARLINGTON VA
PDF Url : ADA298914
Report Date : 30 JUN 1994
Pagination or Media Count : 3
Abstract : The research during this time period has focused on achieving more accurate realizations of the SME filter that than based on the Extended Kalman Filter (EKF). Simulation studies have shown that the linearization carried out in the EKF implementation can result in degraded performance in certain situations. To correct this, we are working on a much more accurate approximation based on the idea of sampling distribution functions. A brief description of the approach is given below. It is well known that the minimumvariance estimate of a random signal x(k) having finite second moment is equal to the conditional mean. Furthermore, when the signal state model and the measurement model are linear and the probability distributions are Gaussian, the conditional mean can be computed from a unique linear combination of the measurement data. Unfortunately, when the signal or measurement model is nonlinear, the solution to the estimation problem has proven to be much more elusive. The traditional approach has been to adapt the linear theory to nonlinear signal and measurement models through the use of the Taylor Series approximation. While this approximation simplifies the nonlinear estimation problem to the point that an implementation is realizable, it has two fundamental drawbacks:
Descriptors : *KALMAN FILTERING, *RESEARCH MANAGEMENT, SIMULATION, MEASUREMENT, MODELS, PROBABILITY DISTRIBUTION FUNCTIONS, TAYLORS SERIES, THEORY, ACCURACY, MOMENTS, ESTIMATES, NONLINEAR SYSTEMS, SIGNALS, LINEARITY, SAMPLING, NONLINEAR ANALYSIS, DISTRIBUTION FUNCTIONS.
Subject Categories : Electrical and Electronic Equipment
Distribution Statement : APPROVED FOR PUBLIC RELEASE