Path: Common/Estimation
% Linear Kalman Filter prediction step.
This assumes a discrete model of the form:
x[k] = a[k-1]x[k-1] + b[k-1]u[k-1] + q
y[k] = h[k]x[k] + r
b and u are optional.
If only one argument is entered it assumes it is a datastructure of
the form d = struct('m',m,'p',','a',a,'q',q,'b',b,'u',u)
or d = struct('m',m,'p',','a',a,'q',q,'bU',b*u)
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Form:
[m, p] = KFPredict( d )
[m, p] = KFPredict( m, p, a, q, b*u )
[m, p] = KFPredict( m, p, a, q, b, u );
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Inputs
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m (n,1) Mean
p (n,n) Covariance matrix
a (n,n) State transition matrix
q (n,n) Model noise matrix
b (n,p) Deterministic input matrix
u (p,1) Deterministic input vector
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Outputs
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m (n,1) Mean
p (n,n) Covariance matrix
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