Touch Position Calculation Algorithm Selection for Noise Immunity
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Solution Overview
Problem
Standard algorithms for calculating conductive object positions on touch panels are sensitive to noise levels and perform poorly in high noise environments, leading to errors and false touch detection.
Innovation Solution
The system employs a position calculation tool that selects from multiple algorithms, such as Gradients, Blais Rioux, Centre of Mass, Linear Interpolation, and Parabolic Estimator, based on the region of touch and noise levels on the capacitive sense array to determine precise touch coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard algorithms are used for calculating touch positions, then the system is simple to implement, but noise immunity deteriorates in high noise environments
Solution Approach 1:
The system dynamically selects position calculation algorithms based on real-time noise conditions and touch regions. Instead of using a fixed algorithm, the system adapts its calculation method by evaluating noise levels and selecting from multiple algorithms (e.g., gradient method, center of mass method, parabolic method) to optimize performance under varying environmental conditions.
Solution Approach 2:
The system changes operational parameters (algorithm selection) based on detected noise levels and touch characteristics. By monitoring noise conditions and adjusting the calculation method accordingly, the system maintains high reliability across different noise environments without requiring a completely complex system architecture.
2Measurement precision
If multiple position calculation algorithms are implemented, then noise immunity improves, but device complexity increases
Solution Approach 1:
The system segments the touch panel into multiple regions and applies different algorithms to different regions based on their characteristics. By dividing the sensing area into zones with different noise profiles and touch patterns, the system can apply optimized algorithms locally, improving overall measurement precision while managing complexity through regional specialization.
Solution Approach 2:
The system uses feedback from noise level detection and touch pattern analysis to automatically select the most appropriate algorithm. The feedback mechanism evaluates current conditions and adjusts algorithm selection in real-time, ensuring high measurement precision without requiring manual intervention or overly complex system design.
3Measurement precision
If region-based algorithm selection is used, then touch detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining touch regions and their characteristic noise profiles before actual touch detection. By establishing region boundaries and expected characteristics in advance, the system can quickly match detected touches to appropriate regions and algorithms without extensive real-time analysis, reducing processing time while maintaining accuracy.
Data Source
AI summary
Apparatuses and methods of position calculation of a touch are described. One method obtains at a processing device touch data of a sense array, the touch data represented as multiple cells. The touch data is for a touch detected proximate the sense array. Noise may be detected on the sense array based on the touch data and a position calculation algorithm from multiple different position calculation algorithms is selected based on the detected noise. The position of the touch proximate the sense array is determined from the touch data based on the selected position calculation algorithm.