```
clear all ;
1 ; % some function declarations
function [ x_pred , p_pred ] = predict( x , P , F , Q )
x_pred = F * x ;
p_pred = F * P *F' + Q ;
endfunction
function [ nu , S ] = innovation( x_pred , p_pred , z , H , R )
nu = z - H * x_pred ; % innovation
S = H * p_pred * H' + R ; % innovation covariance
endfunction
function [ x_new , p_new ] = innovation_update( x_pred , p_pred , nu , S , H )
K = p_pred * H' / S ; % Kalman gain
x_new = x_pred + K * nu ; % new state
p_new = p_pred - K * S * K' ; % new covariance
endfunction
pkg load signal ; % for xcorr function
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Script to generate some "true" constant velocity data for later assessment
%%% Generates:
%%% x: the state history which evolves according to
%%% x(k+1) = Fx(k) + w(k)
%%% w: the process noise history (randomly generated)
%%% z: a set of observations on the state corrupted by noise
%%% v: the noise on each observation (randomly generated)
N = 100 ;
delT = 1 ;
F = [ 1 delT ;
0 1 ] ;
H = [ 1 0 ] ;
% process and measurement noise variances
sigma2Q = 0.01 ;
sigma2R = 0.1 ;
% process covariance matrix
Q = sigma2Q * [ delT/3 delT/2 ;
delT/2 delT ] ;
P = Q ;
R = sigma2R * [ 1 ] ;
x = zeros( 2 , N ) ;
w = zeros( 2 , N ) ;
z = zeros( 1 , N ) ;
v = zeros( 1 , N ) ;
for ii = 2 : N
w( : , ii ) = randn( 2 , 1 ) .* sigma2Q ; % generate process noise
x( : , ii ) = F * x( : , ii - 1 ) + w( : , ii ) ; % update state
v( : , ii ) = randn( 1 ) * R ; % generate measurement noise
z( : , ii ) = H * x( : , ii ) + v( : , ii ) ; % get "true" measurement
endfor
% visualise data
%figure( 1 ) ; plot( x( 1 , : ) , 'k' , 'linewidth' , 2 ) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Script to generate some "true" constant acceleration data for later assessment
%%% Generates:
%%% x: the state history which evolves according to
%%% x(k+1) = Fx(k) + w(k)
%%% w: the process noise history (randomly generated)
%%% z: a set of observations on the state corrupted by noise
%%% v: the noise on each observation (randomly generated)
N = 100 ;
delT = 1 ;
F = [ 1 delT delT/2 ;
0 1 delT ;
0 0 1 ] ;
H = [ 1 0 0 ] ;
% process and measurement noise variances
sigma2Q = 0.01 ;
sigma2R = 0.1 ;
% process covariance matrix
Q = sigma2Q * [ delT/20 delT/8 delT/6 ;
delT/8 delT/3 delT/2 ;
delT/6 delT/2 delT ] ;
P = Q ;
R = sigma2R * [ 1 ] ;
x = zeros( 3 , N ) ;
w = zeros( 3 , N ) ;
z = zeros( 1 , N ) ;
v = zeros( 1 , N ) ;
for ii = 2 : N
w( : , ii ) = randn( 3 , 1 ) .* sigma2Q ; % generate process noise
x( : , ii ) = F * x( : , ii - 1 ) + w( : , ii ) ; % update state
v( : , ii ) = randn( 1 ) * R ; % generate measurement noise
z( : , ii ) = H * x( : , ii ) + v( : , ii ) ; % get "true" measurement
endfor
% visualise data
%figure( 1 ) ; plot( x( 1 , : ) , 'k' , 'linewidth' , 2 ) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Octave script to assess Kalman filter performance
%%% The script assumes the existence of a vector z of
%%% noise corrupted observations
N = length( z ) ; % number of Klamn filter iterations
Qfactor = 1 ; % process noise mult factor
Rfactor = 1 ; % measurement noise mult factor
delT = 1 ; % time step
F = [ 1 delT ;
0 1 ] ; % update matrix
H = [ 1 0 ] ; % measurement matrix
sigmaQ = Qfactor * sqrt( 0.01 ) ;
sigmaR = Rfactor * sqrt( 0.1 ) ;
Q = sigmaQ^2 * [ 1/3 1/2 ;
1/2 1 ] ; % process noise covariance matrix
P = 10 * Q ;
R = sigmaR^2 * [ 1 ] ; % measurement noise covariance
% allocate space prior to loop
xhat = zeros( 2 , N ) ; % state estimate
nu = zeros( 1 , N ) ; % innovation
S = zeros( 1 , N ) ; % innovation (co)variance
q = zeros( 1 , N ) ; % normalised innovation squared
for ii = 2 : N
% predict using update matrix F and updated values of P and Q from previous ii loop
% x_pred is the prediction of state values
% p_pred is the prediction of the covariance matrix P given previous P and Q
[ x_pred , p_pred ] = predict( xhat( : , ii - 1 ) , P , F , Q ) ;
% measurement
% "nu" is difference between predicted values and measured values of the states,
% given the measurement matrix H and measurement noise R
% "S" is a measurement of how the covariance matrix P is predicted to have changed
% during the above prediction step, given that this cannot actually be known or
% directly measured as not all the underlying state changes can be directly measured.
[ nu( : , ii ) , S( : , ii ) ] = innovation( x_pred , p_pred , z( ii ) , H , R ) ; % orig
% update step updates the state estimates and covariance matrix P using the Kalman gain,
% which is internally calculated in the "innovation_update" function
[ xhat( : , ii ) , P ] = innovation_update( x_pred , p_pred , nu( : , ii ) , S( : , ii ) , H ) ;
% q is just a record keeping vector for later analysis of normalised innovation squared
q( : , ii ) = nu( : , ii )' * inv( S( : , ii ) ) * nu( : , ii ) ;
endfor
sumQ = sum( q ) ; % determine Sum q which is Chiˆ2 on N d.o.f.
r = xcorr( nu ) ; % get autocorrealtion of innovation
% plot state and state estimate
subplot( 2 , 2 , 1 ) ; plot( x( 1 , : ) , 'k' , 'linewidth' , 2 , xhat( 1 , : ) , 'r' , 'linewidth' , 2 ) ;
title( 'State and State Esimate' ) ;legend( 'State' , 'State Estimate' ) ;
% plot innovation and 2sigma confidence interval
subplot( 2 , 2 , 2 ) ; plot( nu , 'b' , 'linewidth' , 2 , 2 * sqrt( S ) , 'r' , -2 * sqrt( S ) , 'r' , ...
zeros(1,N) , 'r.' , 'linewidth' , 1 ) ;
title( 'Innovation and 2sigma confidence intervals' ) ; legend( 'Innovation' , '2Sigma Levels' ) ;
% plot normalised innovation squared
subplot( 2 , 2 , 3 ) ; plot( q , 'k' , 'linewidth' , 2 , mean( q ) .* ones( 1 , N ) , 'r' , 'linewidth' , 2 ) ;
title( 'Normalised innovation squared' ) ;
% plot autocorrelation of innovation (normalised)
subplot( 2 , 2 , 4 ) ; plot( r( N : 2 * N - 1 ) / r( N ) , 'k' , 'linewidth' , 2 , zeros(1,N) , 'k.' , 'linewidth' , 1 ) ;
title( 'Autocorrelation of innovation (normalised)' ) ;
```

Soon I shall be using this code, with some additions perhaps, to test various Kinematic model implementations of Kalman filters on financial time series with a view to identifying which models are suitable or not.More in the near future.