Tuning & Comparison: RMSE for Several Q/R Pairs vs a Moving Average and an Alpha–Beta Filter
This lesson is part of the course
Buy the course once for $9.99 to unlock every lesson and lab, the final exam and the certificate – lifetime access, no subscription.
Lessons in this course
- Noisy Measurements: True Value vs Readings and the Running Average as a Recursive Update Pro
- Variances and Weighting: Inverse-Variance Weights, Combining Two Readings and the Uncertainty After Combining Pro
- The 1-D Filter: Predict, the Kalman Gain K = P/(P+R) and Update in a Step Table Pro
- Process Noise: Q vs R, How the Gain Settles, and Lag vs Noise in a Chart Pro
- Constant-Velocity Model: Position and Velocity States and the 2×2 Covariance as Four Cells Pro
- Sensor Fusion: Two Sensors with Different Noise, Sequential Updates and the Fused Estimate Pro
- Missing and Outlier Readings: Skipping the Update and a Gating Rule on the Innovation Pro
- Tuning & Comparison: RMSE for Several Q/R Pairs vs a Moving Average and an Alpha–Beta Filter Pro