• reference: scipy.signal.butter, Mathnet.Filtering.Butterworth
• reading: https://blog.csdn.net/zengxy3407/article/details/132035202
• reading: https://blog.csdn.net/u013600306/article/details/142703250

What is Kalman Filter?

A recursive way to compute conditional expectation.

(The KF was discussed in page 47-48 of the paper.)

Kalman filter: This technique has been used by several studies as a sensor fusion method. Thus, it is not a method to extract breathing parameters but to fuse measurements from different sensors. When multiple respiration sensors are available, the measurements they provide are not exactly the same. Furthermore, measurements always contain noise. The Kalman filter is used to provide a final value based on the measurements of the different sensors, the model of variation of the breathing parameter, the noise model of the sensors, and the variation model [270]. Figure 33 shows an overview of the Kalman filter algorithm adapted to the fusion of breathing sensors