Adaptive Digital Filter Order Adjustment for Touch Sensing Noise
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Solution Overview
Problem
Touch display systems face challenges in maximizing touch sensitivity due to noise interference, which affects the accuracy of touch input recognition and response delay times, especially in noisy environments like mobile devices.
Innovation Solution
An adaptive digital filtering method that estimates the signal-to-noise ratio (SNR) and dynamically adjusts the digital filter order to optimize touch data filtering, reducing noise and improving response times by selecting an appropriate digital filter tap based on the estimated SNR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital filtering is applied to reduce noise in touch data, then touch sensitivity and accuracy are improved, but response delay time increases
Solution Approach 1:
The patent applies dynamics by making the filter order adaptive rather than fixed. The filter order is dynamically adjusted based on the estimated signal-to-noise ratio (SNR) of the touch data. When noise levels are high, a higher filter order is applied to maximize noise reduction and touch sensitivity. When noise levels are low, the filter order is reduced to minimize processing delay and improve response time. This dynamic adaptation resolves the contradiction between touch sensitivity and response delay time.
Solution Approach 2:
The patent changes the filter order parameter based on SNR estimation. By estimating the SNR of the incoming touch data and selecting an appropriate filter order from multiple available orders, the system optimizes the balance between noise reduction (improving touch sensitivity) and processing speed (reducing response delay). This parameter change approach allows the system to adapt to varying noise conditions in real-time.
2Object-affected harmful factors
If higher order digital filter is used to reduce noise, then noise reduction performance is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the filter order based on SNR estimation rather than using a fixed high-order filter. This means the computational complexity is adjusted in real-time according to the actual noise conditions. When noise is high, a higher filter order is applied to maximize noise reduction. When noise is low, a lower filter order is used to reduce unnecessary computational complexity. This dynamic approach optimizes the balance between noise reduction performance and computational resource consumption.
Solution Approach 2:
The patent changes the filter order parameter based on SNR estimation to optimize the balance between noise reduction and computational complexity. By estimating the SNR and selecting an appropriate filter order from multiple available orders, the system avoids the constant high computational complexity of fixed high-order filters while maintaining effective noise reduction when needed. This parameter adaptation resolves the contradiction between noise reduction performance and computational complexity.
3Measurement precision
If adaptive filter order adjustment is implemented, then touch data accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent implements preliminary action by pre-establishing multiple filter orders and SNR estimation mechanisms before actual touch data processing. The system prepares a set of filter orders with different characteristics and develops the SNR estimation algorithm in advance. When touch data arrives, the system quickly estimates the SNR and selects the appropriate pre-prepared filter order, avoiding the need for complex real-time filter design and reducing processing overhead during actual touch operations.
Solution Approach 2:
The system performs self-service by automatically estimating SNR and selecting appropriate filter orders without requiring external intervention or complex manual configuration. The SNR estimation unit automatically analyzes the touch data characteristics and the filter selection unit autonomously chooses the optimal filter order, reducing the processing overhead that would otherwise be required for manual tuning and configuration of filter parameters for different noise conditions.
Data Source
AI summary
An adaptive digital filtering method and apparatus for touch data in a touch system is described. Adaptive digital filtering involves estimating a signal-to-noise ratio (SNR) for stored touch data, and reducing a digital filter order if the SNR is high and increasing the digital filter order if the SNR is small.