Tracking Touched Trajectory on Capacitive Touch Panels Using an Adjustable Weighted Prediction Covariance Matrix

Abstract

This study presents a smooth tracking algorithm for a proposed capacitive touch panel system that consists of a 7-in panel, a microcontroller unit, a sensor IC, an interface board, and a laptop computer. The proposed adaptive Kalman filter (AKF) algorithm is capable of reducing the complexity of the computation by the processor and the effect of unstable measurement noise on zigzag trajectory as the speed of the tracked trajectory varies. Experimental results indicate that the various reporting rates decrease estimative ability of touched position, which is solved by using an AKF method to achieve the accurate estimation of touched position. The results also demonstrate that the proposed method can accurately match the reference trajectory compared with the trajectories of the moving average filter and the Kalman filter. The root-mean-square errors of the proposed AKF method are all lower than 0.5 cm under the specifications of various movement speeds and reporting rates.