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function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

CM Input file (NEC-2) for an interlaced combination of a 2-element yagi (40 m) CM and a 3-element (20 m). This is a model to give max F/B on the same freq CM (7 MHz) as measurement done 970524. To a

function [a,A,g]=polyfit(x,y,n) %x,y is measurement data,n is the degree of polyfitting polynomial,r is %weight function,the r defauts is set to 1 %x=[0.0 0.1 0.2 0.3 0.5 0.8 10];y=[1.0 0.41 0.50 0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0

function [residual, estimate] = kalman_filter(npts, T, X0, inp, R, nvar) N = npts; rn=1; % read the intial estimate for the state vector X = X0; % it is assumed that the measurement vector H=[1,0