Hello everyone!!

Im struggling on a simple assignment. I have to filter specific data

(which are in .xls format) using the "optimal" kalman filter in both

ARMAX and ARX models. the struggle is on how to connect the data from

the xls file to the filter...

here i give u my kalman filter with the comments

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% 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(0,Q) meaning w is gaussian noise with covariance Q

% v ~ N(0,R) meaning v is gaussian noise with covariance R

%

% VECTOR VARIABLES:

%

% s.x = state vector estimate. In the input struct, this is the

% "a priori" state estimate (prior to the addition of the

% information from the new observation). In the output struct,

% this is the "a posteriori" state estimate (after the new

% measurement information is included).

% s.z = observation vector

% s.u = input control vector, optional (defaults to zero).

%

% MATRIX VARIABLES:

%

% s.A = state transition matrix (defaults to identity).

% s.P = covariance of the state vector estimate. In the input struct,

% this is "a priori," and in the output it is "a posteriori."

% (required unless autoinitializing as described below).

% s.B = input matrix, optional (defaults to zero).

% s.Q = process noise covariance (defaults to zero).

% s.R = measurement noise covariance (required).

% s.H = observation matrix (defaults to identity).

%

% NORMAL OPERATION:

%

% (1) define all state definition fields: A,B,H,Q,R

% (2) define intial state estimate: x,P

% (3) obtain observation and control vectors: z,u

% (4) call the filter to obtain updated state estimate: x,P

% (5) return to step (3) and repeat

%

% INITIALIZATION:

%

% If an initial state estimate is unavailable, it can be obtained

% from the first observation as follows, provided that there are the

% same number of observable variables as state variables. This "auto-

% intitialization" is done automatically if s.x is absent or NaN.

%

% x = inv(H)*z

% P = inv(H)*R*inv(H')

%

% This is mathematically equivalent to setting the initial state estimate

% covariance to infinity.

function s = kalmanf(s)

% set defaults for absent fields:

if ~isfield(s,'x'); s.x=nan*z; end

if ~isfield(s,'P'); s.P=nan; end

if ~isfield(s,'z'); error('Observation vector missing'); end

if ~isfield(s,'u'); s.u=0; end

if ~isfield(s,'A'); s.A=eye(length(x)); end

if ~isfield(s,'B'); s.B=0; end

if ~isfield(s,'Q'); s.Q=zeros(length(x)); end

if ~isfield(s,'R'); error('Observation covariance missing'); end

if ~isfield(s,'H'); s.H=eye(length(x)); end

if isnan(s.x)

% initialize state estimate from first observation

if diff(size(s.H))

error('Observation matrix must be square and invertible for

state autointialization.');

end

s.x = inv(s.H)*s.z;

s.P = inv(s.H)*s.R*inv(s.H');

else

% This is the code which implements the discrete Kalman filter:

% Prediction for state vector and covariance:

s.x = s.A*s.x + s.B*s.u;

s.P = s.A * s.P * s.A' + s.Q;

% Compute Kalman gain factor:

K = s.P*s.H'*inv(s.H*s.P*s.H'+s.R);

% Correction based on observation:

s.x = s.x + K*(s.z-s.H*s.x);

s.P = s.P - K*s.H*s.P;

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Thanks for your time and help

nick