This node applies a Kalman Filter to up to three values on its inputs.
Kalman FIltering, also known as linear quadratic estimation (LQE) is an algorithm that produces estimates using a series of measurements over time effectively filtering noise.
Because this algorithm produces estimates it relies on iterations of prediction-measurement cycles.
Process noise: Affects the weight of predictions in the algorithm. Lower values (<0.1) will give smoother signals but less accurate predictions. Prefer values between 0 and 0.1.
Default : 0.0
Measurement noise: Affects the weight of measures in the algorithm. Higher values (>10) will give smoother signals but less accurate values.
Default : 5.0
Error cov. post: Affects uncertainty in the initialisation of the algorithm. If your starting position is accurate, prefer lower values. If not, prefer higher values.
Default : 0.01
Delay: Useful when compensating for projector input lag. Leave at 0 otherwise. Unit : seconds
Default : 0.0
Force Evaluate: When toggled ON, will evaluate a prediction even if the input does not change.
Default : OFF
Interpolation: (Only visible when
Force Evaluateis OFF) Adds an interpolation to the values based on the algorithm. Unit : milliseconds
Default : 0
Predictions / Measures noise
Process noise and Measurement noise are complimentary properties. One should be rather low while the other is rather high :
- If Process noise is low : we trust the predictions to be accurate, Measurement noise can be higher to reduce noise in our measures.
In this case the output will be smoother but may have a higher latency (delay).
- If Measurement noise is low : we trust our measures to be accurate, Process noise can be higher to reduce noise in the predictions.
In this case the output will have a lower latency but may be noisier (jerk).
|X||Float||First value to filter|
|Y||Float||Second value to filter|
|Z||Float||Third value to filter|
|X||Float||First value filtered|
|Y||Float||Second value filtered|
|Z||Float||Third value filtered|
The filter is using default values on all its properties.
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