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% mudm1.m % Scope: This MATLAB macro implements the discrete Kalman filter measurement % updating using Bierman's U-D measurement update algorithm. On

% timetr.m % Scope: This MATLAB macro computes GPS time of transmission from time of % measurement (reception) for a satellite pseudorange measurement; %

function [K,Pout] = josephb(z,R,H,P) % % Joseph "stabilized" Kalman filter measurement % update as modified by Bierman. % T1 = sqrt(R); T2 = H/T1; T4 = P*T2'; T5 = T2*T4 + 1; K = T4/T5; T7

function [K,Pout] = joseph(z,R,H,P) % % P. D. Joseph's "stabilized" Kalman filter measurement % update. % n = length(H); zp = sqrt(1/R); Hp = zp*H; K = (Hp*P*Hp' + 1) \ P*Hp'; W = eye

function [K,Pout] = josephdv(z,R,H,P) % % Joseph "stabilized" Kalman filter measurement % update as implemented by De Vries. % T1 = P*H'; T2 = H*T1 + R; K = T1/T2; T3 = .5*K*T2 - T1; T4 = T3

function z= add_observation_noise(z,R, addnoise) %function z= add_observation_noise(z,R, addnoise) % % Add random measurement noise. We assume R is diagonal. if addnoise == 1 len= size(

function observe_heading(phi, useheading) %function observe_heading(phi, useheading) % % Perform state update for a given heading measurement, phi, % with fixed measurement noise: sigmaPhi global

%PREDICT Measurement prediction of alpha,r-line features. % [LR,HR,HM] = PREDICT(L,XR,CR,CRL) transforms the alpha,r-line % feature L represented in the world frame into the robot frame % given

%MESG Display progress information. % MESG subroutine displays progress status when performing MIMO system % performance measurement. Therefore it makes no sense to run this % routine alone.

%KF_LHOOD Kalman Filter measurement likelihood % % Syntax: % LH = KF_LHOOD(X,P,Y,H,R) % % In: % X - Nx1 state mean % P - NxN state covariance % Y - Dx1 measurement vector. % H - Me