Sensor Fusion: Two Sensors with Different Noise, Sequential Updates and the Fused Estimate
This lesson is part of the course
In this lesson
You fuse a noisy and a precise level sensor in one filter step by two sequential updates, then reach the same estimate and variance in one go with inverse-variance weights.
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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