Noisy Measurements: True Value vs Readings and the Running Average as a Recursive Update
This lesson is part of the course
In this lesson
You turn ten noisy level readings into a running average that is updated one reading at a time, check that it equals the plain average, and compare its error with the error of a single reading.
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 Locked
- Variances and Weighting: Inverse-Variance Weights, Combining Two Readings and the Uncertainty After Combining Locked
- The 1-D Filter: Predict, the Kalman Gain K = P/(P+R) and Update in a Step Table Locked
- Process Noise: Q vs R, How the Gain Settles, and Lag vs Noise in a Chart Locked
- Constant-Velocity Model: Position and Velocity States and the 2×2 Covariance as Four Cells Locked
- Sensor Fusion: Two Sensors with Different Noise, Sequential Updates and the Fused Estimate Locked
- Missing and Outlier Readings: Skipping the Update and a Gating Rule on the Innovation Locked
- Tuning & Comparison: RMSE for Several Q/R Pairs vs a Moving Average and an Alpha–Beta Filter Locked