||Implementation of a Pressure Sensing and Smooth Tracking Algorithms for Capacitive Touch Panels
||Department of Electrical Engineering
Capacitive type panel
pressure sensing stylus
stroke reconstruction algorithm
fuzzy logic system
strong tracking Kalman filter
The capacitive touch panel (CTP) has attracted a significant amount of interest and achieved considerable penetration of the consumer electronics products market in recent years owing to its sensitivity, excellent durability and multi-touch functionality. However, the CTP is easily affected by noise produced by the trembling of a finger, environmental magnetic interference, display noise, or process variation. Moreover, when a user draws with different speeds, the measurement noise caused by the sensor IC induces an error in the touched position and zigzagged trajectory, especially when the motion is slow. Although the well-known moving average filter (MAF) method is frequently used to reduce the measurement noise, it needs a large number of points in a specific interval to filter out a significantly high frequency noise, leading to trajectory delay and amplitude decay.
This dissertation proposes three novel touch algorithms and verifies their effectiveness by experiment and measurement results. The touch algorithm of Kalman filter (KF) firstly is adopted to reduce the noise effect, and is combined with the stroke reconstruction algorithm to detect touch pressure without increasing hardware costs or the need for a power source for the stylus. The results of experiments on the proposed CTP system were analyzed, demonstrating the effectiveness of the proposed stylus and its stroke reconstruction algorithm. Moreover, the robust tracking algorithm of the particle filter (PF) as second touch algorithm is utilized to overcome the problem of modeling error in the KF method, which accurately estimates the touched position and trajectory when the touch movement changes rapidly with a nonlinear trajectory. Experimental results demonstrate that regardless of linear and nonlinear scenarios, the PF offers better root mean square error (RMSE) of linear and nonlinear tracking trajectories than that of KF. Furthermore, in the third touch algorithm, to reduce the computation cost and maintain the trajectory smoothness, the algorithm based on KF of the mixed strategy is proposed by using the fuzzy logic-based adaptive strong tracking Kalman filter (FLASTKF), which effectively mitigates the effect of variation of measurement noise and supplies accurate estimation of the touched position. In particular, this work also provides a novel method to measure and quantify the "smoothness" of a touched trajectory. The experimental results indicate that the proposed FLASTKF method successfully achieves the a smooth tracking trajectory, regardless of speed, as well as decreases the mean tracking error by 85.4% over that achieved using the MAF.
List of figures viii
List of tables xi
CHAPTER 1 Introduction 1
1.1. Background 1
1.2. Motivation 3
1.3. Dissertation organization 10
CHAPTER 2 Stroke reconstruction algorithm based on Kalman filter for touchscreen panel 11
2.1. Introduction 11
2.2. Proposed touchscreen panel configuration 13
2.3. Stroke reconstruction algorithm 16
2.4. Experiment results 19
2.5. Summary 23
CHAPTER 3 Touch position tracking based on particle filter for capacitive touch panels 24
3.1. Introduction 24
3.2. Particle filter for tracking on CTP system 26
3.3. Experimental results 30
3.4. Summary 35
CHAPTER 4 Position estimation and smooth tracking with a fuzzy logic-based adaptive strong tracking Kalman filter for capacitive touch panels 36
4.1. Introduction 36
4.2. Signal processing for CTP system 38
4.3. FLASTKF for smooth tracking algorithm 41
4.4. Experimental results 50
4.5. Summary 61
CHAPTER5 Conclusion and future work 62
5.1. Conclusion 62
5.2. Future work 64
Publication List 69
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