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In order to solve the accuracy and delay problems in the traditional flow speed estimation method in the sewage pipe network system, we develop a kind of multi-sensor fusion estimation method. Autoregressive moving average model is used in the estimation process, 3 kinds of fusion method is compared. An example is presented to verify the algorithm, Matlab is used to caculate, and some pictures to explain our points.

In this paper, we describe the formatting guidelines for For the multi-sensors time-invariant system with unknown parameters, in order to improve the accuracy of the identification, based on the ARMA model, a kind of multi-steps identification algorithm is presented. Step a: we use recursive extended least squares method to get the local estimates of the unknown parameters, we use arithmetic mean to get the first fusion estimates of the parameters. Step b: we use correlated function method to get the estimates of observation noises of every single sensor. Step c is the key step of our algorithm which differs from the conventional 2-steps algorithm. Step c: we take the related information of the estimates of every single sensor as the weight, we take the weighted fusion of the local estimates as the final estimates of the parameters. Compared to the real values, the final estimates are more accurate than the first estimates. We use Matlab to simulate a typical example, the simulation results show that the final estimates have better convergence than the first estimates, which could show the accuracy advantage of the multi-steps identification algorithm presented in this paper.

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.

Experimental box method is always used in the experimental course in Chinese university curriculum plan. There exist some problems in the experimental box method, such as the experiments are stylized and lack of diversity. In this paper, based on matlab simulation, for a problem in the lecture of control theory, various changes of the problem are presented. For these changes, system identification method and kalman filtering method are used to solve these problems. matlab is used to simulate these problems, the simulation results shows the effectiveness of the algorithms. This simulation method based on matlab could strengthen the diversity of the experiments and deepen the students' understanding of the lecture.