Troubleshooting Kalman Filtering for INS/GPS Integration

In summary, the speaker is having trouble implementing a Kalman filtering algorithm for an INS/GPS integration system. They are using equations from a specific chapter and have noticed that the estimate error is very small on the graphic. They have also found a possible mistake in their use of a dynamic matrix and are wondering how to express FF in programming. However, they later mention that they have solved their problem.
  • #1
shtuceron
3
0
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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  • #2
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?
 
  • #3
Thanks, i'am solve my problems.
 

1. What is Kalman Filtering?

Kalman Filtering is an algorithm used to estimate the state of a system by combining noisy measurements with a mathematical model of the system. It is commonly used in navigation and control systems to improve the accuracy of sensor data.

2. How does Kalman Filtering work?

Kalman Filtering works by predicting the state of a system based on its previous state and the system dynamics, and then comparing this prediction with new measurements. The algorithm then calculates a weighted average of the predicted and measured states to estimate the most accurate state of the system.

3. What is the purpose of integrating INS and GPS with Kalman Filtering?

By integrating INS (Inertial Navigation System) and GPS (Global Positioning System) data with Kalman Filtering, we can take advantage of the strengths of both systems. INS provides accurate, high frequency measurements, while GPS provides absolute position information. Combining these with Kalman Filtering can improve the accuracy and stability of the navigation system.

4. What are some common challenges in troubleshooting Kalman Filtering for INS/GPS Integration?

Some common challenges include dealing with noisy sensor data, selecting appropriate system dynamics and measurement models, and tuning the Kalman Filter parameters. It is also important to consider the effects of sensor biases and errors, as they can significantly affect the accuracy of the estimated state.

5. How can I verify the performance of a Kalman Filter for INS/GPS Integration?

To verify the performance of a Kalman Filter, you can compare the estimated states with ground truth data, if available. It is also important to conduct thorough testing and analysis of the filter's behavior in different scenarios and conditions. Additionally, monitoring the filter's innovation and estimation error can provide insight into its performance.

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