资源简介
卡尔曼程序 UKF程序 matlab,调试已经通过!欢迎探讨!
代码片段和文件信息
% KALMANF - updates a system state vector estimate based upon an
% observation using a discrete Kalman filter.
%
% Version 1.0 June 30 2004
%
% This tutorial function was written by Michael C. Kleder
%
% INTRODUCTION
%
% Many people have heard of Kalman filtering but regard the topic
% as mysterious. While it‘s true that deriving the Kalman filter and
% proving mathematically that it is “optimal“ under a variety of
% circumstances can be rather intense applying the filter to
% a basic linear system is actually very easy. This Matlab file is
% intended to demonstrate that.
%
% An excellent paper on Kalman filtering at the introductory level
% without detailing the mathematical underpinnings is:
% “An Introduction to the Kalman Filter“
% Greg Welch and Gary Bishop University of North Carolina
% http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html
%
% PURPOSE:
%
% The purpose of each iteration of a Kalman filter is to update
% the estimate of the state vector of a system (and the covariance
% of that vector) based upon the information in a new observation.
% The version of the Kalman filter in this function assumes that
% observations occur at fixed discrete time intervals. Also this
% function assumes a linear system meaning that the time evolution
% of the state vector can be calculated by means of a state transition
% matrix.
%
% USAGE:
%
% s = kalmanf(s)
%
% “s“ is a “system“ struct containing various fields used as input
% and output. The state estimate “x“ and its covariance “P“ are
% updated by the function. The other fields describe the mechanics
% of the system and are left unchanged. A calling routine may change
% these other fields as needed if state dynamics are time-dependent;
% otherwise they should be left alone after initial values are set.
% The exceptions are the observation vectro “z“ and the input control
% (or forcing function) “u.“ If there is an input function then
% “u“ should be set to some nonzero value by the calling routine.
%
% SYSTEM DYNAMICS:
%
% The system evolves according to the following difference equations
% where quantities are further defined below:
%
% x = Ax + Bu + w meaning the state vector x evolves during one time
% step by premultiplying by the “state transition
% matrix“ A. There is optionally (if nonzero) an input
% vector u which affects the state linearly and this
% linear effect on the state is represented by
% premultiplying by the “input matrix“ B. There is also
% gaussian process noise w.
% z = Hx + v meaning the observation vector z is a linear function
% of the state vector and this linear relationship is
% represented by premultiplication by “observation
% matrix“ H. There is also gaussian measurement
% noise v.
% where w ~ N(0Q) me
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 4326 2010-12-12 05:46 ukf.m
文件 7652 2012-02-28 11:24 kalmanf.m
----------- --------- ---------- ----- ----
11978 2
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