Troubleshooting Kalman Filtering for INS/GPS Integration

AI Thread Summary
The discussion focuses on troubleshooting Kalman filtering in an INS/GPS integration system. The user is experiencing issues with the estimated error being very small, despite the error graphic showing good results. They reference equations from a specific chapter on strapdown inertial navigation and express confusion about the discrete form of the system error equation. The user seeks guidance on implementing the system transition matrix, FF, as an exponential function in programming. Clarification on this implementation is requested to resolve their issues.
shtuceron
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Hello all.

I have some problem with realisation Kalman filtering in INS/GPS integration system.

I use equation from "Strapdown Inertial Navigaation" chapter 12. and trying programming Kalman filtering (discret), but have some trouble, estimate error have very small value on graphic, but error graphic have best result, why is it?

for example:
2qsml91.png
(if i zoom it is really good)Kalman filtering algorithm:

S = F*P*transpose(F) + G*Q*transpose(G);

K = S*transpose(H)*(H*S*transpose(H) + R)^-1;

Pout = (eye(15,15) - K*H)*S;

EXout = F*EX + K*(Z - H*F*EX);
 
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It seems that I can find mistake, I use dynamic matrix from chapter 12, but on 404 page (chapter 13) wrote, that:"in Oder to allow a discrete Kalman filter to be constructed, it is necessary to express the system error equation (13.1) in discrete form. If ex represents the inertial navigation system error states at time tk and t(k+1) the error states at time t(k+1) we may write: dx =FF*dx + wk, where FF is the system transition matrix at time tk, which may be expressed in terms of the system matrix F as follows: FF = exp[F(t(k+1) - tk)]"
my question : how can I realize FF = exp[F(t(k+1) - tk)] in programming?
 
Thanks, i'am solve my problems.
 
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