Self-tuning Kalman Filter for the City Sewage Treatment System
Heng Li, Sun Hui-fen, Hao Wang · 2017
This paper presents a kind of self-tuning kalman filtering algorithm which could deal with the city sewage treatment system with unknown colored noises. This is a typical problem in the lecture "Control Theory". The algorithm includes 3 steps. In the first step, all the data should be normalized as the form of matrix, and the colored noise is whiten by recursive operation; in the second step, the correlation function is obtained to calculate the estimations of the variance of the noise; in the third step, the estimations are taken into the optimal kalman filter to get the corresponding self-tuning filter. We set a testing point of the sewage pipe, the flow and the velocity of the sewage pipe both have good convergence to the corresponding optimal kalman filter. An example of a 3-sensors system is simulated by matlab to show the effectiveness of this self-tuning kalman filter.