Neural calibration and Kalman filter position estimation for touch panels

This paper develops methodologies and techniques for calibration and dynamic touching position estimation of touch panels using adaptive linear neural networks (ALNN) and Kalman filter. A neural-based calibration method is proposed to determine nonlinear mapping relationships of measured and known touch points, thereby calibrating their positions in a real-time manner. In order to obtain position estimation of fast moving points in the drawing mode, a Kalman filtering scheme is proposed to achieve satisfactory precision. Numerous simulation results are provided to show the effectiveness and feasibility of the proposed ALNN method and the Kalman filter estimation algorithm. Experimental results are described which have been conducted to show that the proposed calibration and estimation approaches perform well for electronic consumer products, such as notebooks, personal digital assistants (PDAs) and etc.