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it unlocks many cool features!
- double fastTs = 1.0 / 100;
- double slowTs = 1.0 / 10;
- Eigen::MatrixXd fastA(n, n);
- Eigen::MatrixXd slowA(n, n);
- fastA << 1, fastTs, 0, 0, 1, fastTs, 0, 0, 1;
- slowA << 1, slowTs, 0, 0, 1, slowTs, 0, 0, 1;
- Eigen::VectorXd FillerState(n);
- FillerState << 0, 0, 0;
- //loop
- for (int i = 0; i < measurements.size(); i++) { //ACTUAL MEASURED SAMPLE
- y << measurements[i];
- //KalmanStates = KalmanFilter(KalmanStates, Measurement, slowTs);
- kf.update(y);
- FillerState << kf.state().x();
- for (int ji = 0; ji < 10; ji++) // TEN PREDICTED SAMPLES
- {
- t += dt;
- FillerState = fastA*FillerState;
- }
- //HOW DO I...
- FillerMeasurements = C*FillerStates; // ??
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