General Instructions:

a. Implementation of correspondence between the initial conditions of the Kalman filter variables, such as the initial state estimate and error covariance, and the filter's prediction performance.

1. This is the Kalman filter Estimation process.
2. You have to select the parameters from the right column.
3. After giving all the parameters you can see the code on the left side by clicking generate code.
4. After that if you want to download the code you can click the download button and run using submit and run .
5. Results will be displayed below the generate code button.

b. To implement the Kalman filter's performance in an unforced dynamic model with a noiseless state-space.

1. This is the kalman filter simulation process
2. You have to select the parameters from the right column.
3. After giving all the parameters you can see the code on the left side by clicking generate code.
4. After that if you want to download the code you can click the download button and run using submit and run .
5. Results will be displayed below the generate code button.