Reducing process and measurement noise and errors in technical processes and sensors
In this research we have demonstrated Kalman Filter (KF) that improves the quality of the measurement of sensor signal. Kalman Filter has long been used to eliminate the process error and measurement noise. Bearing in mind that almost all industrial automation and control systems are stock with process errors and measurement noises, we tried to implement Kalman Filtering algorithm to typical processes that measure the height of the water level of a tank and the angle of deviation of the wheel of a Mobile Robot (MR) from a predefined guided path. First a simulation study was conducted using the developed Kalman Filter algorithm. The algorithm was then translated and transferred to a real-word implementation domain which is an electronic computing module. While a level detector (pressure sensor) was used to sense the height of the real-time water levels under filling, dropping, both conditions, the LVDT transducer, developed in the laboratory was used to measure the angle of deviation of a MR’s track in a lab room experimental setting. It was observed from the results that process error and measurement noise can be eliminated using KF. The paper systematically presents the results after reviewing the theoretic model of the KF and the application of families of KFs. We were able to reduce the errors and noise from about 15% to 5% using KF technique.