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function [y] = bshfun(x,u,t); % PURPOSE : Measurement model function. % INPUTS : - x: The evaluation point in the domain. % OUTPUTS : - y: The value of the function at x. % AUTHORS : Nando de Fre

function [y] = hfun(x,t); % PURPOSE : Measurement model function. % INPUTS : - x: The evaluation point in the domain. % OUTPUTS : - y: The value of the function at x. % AUTHORS : % DATE : if

function [y] = ukf_hfun(x,u,n,t); % PURPOSE : Measurement model function fpr UKF. % INPUTS : - x: Hidden state % : - u: control vector % - n: Measurement noise % - t

% LGSLAM - Performs a filter update for a linear-Gaussian SLAM % filter (using the unscented transformation to % linearize nonlinear motion and measurement models). % % This fu

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 R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec

function R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec

function R=cov_w(obj,t); % Returns the covariance matrix of the measurement noise, at time t. % % Syntax: (* = optional) % % R = cov_e(model, t); % % In arguments: % % 1. model % Model objec