Analog Signal Filtering With Dynamic Threshold Noise Rejection
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Solution Overview
Problem
Existing signal processing technologies face challenges in effectively filtering analog signals from sensors, particularly when noise is present, as they often fail to maintain accurate information transmission in noisy conditions.
Innovation Solution
The method involves sampling analog signals, calculating cumulative changes in signal values relative to previous samples, and comparing these changes to predetermined thresholds to determine if the signal has exceeded noise limits, thereby maintaining the signal at a previous value if noise is detected, ensuring the filtered signal remains reliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise filtering methods are applied to analog signals, then noise removal is achieved, but signal information accuracy deteriorates in dynamic conditions
Solution Approach 1:
The patent implements dynamic threshold adjustment based on signal characteristics. The filtering threshold is not fixed but adapts according to the cumulative change magnitude and rate of change of the signal, allowing the system to distinguish between noise and genuine signal variations in different operating conditions
Solution Approach 2:
The patent changes the parameter of threshold values from static to dynamic. By calculating cumulative changes over multiple samples and comparing against dynamically determined thresholds, the system adjusts its sensitivity based on the actual signal behavior, maintaining accuracy while filtering noise
2Measurement precision
If signal sampling rate is increased to capture more information, then measurement precision improves, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential information from sampled signals by calculating cumulative changes rather than processing every raw sample. This selective extraction of meaningful variations reduces processing complexity while maintaining the ability to detect genuine signal changes
Solution Approach 2:
The patent uses a multi-sample cumulative change approach that examines more samples than a single-point measurement would require. By evaluating cumulative changes across multiple samples (excessive action), the system achieves higher reliability in distinguishing noise from genuine variations without requiring excessively high sampling rates
3Reliability
If aggressive filtering is applied to remove noise, then signal reliability improves, but information transmission accuracy deteriorates
Solution Approach 1:
The patent employs dynamic thresholding that adapts to signal characteristics. The threshold for filtering is adjusted based on the cumulative change magnitude and rate of change, allowing the system to be more permissive during genuine signal transitions and more aggressive during noise conditions, thus maintaining both reliability and information accuracy
Solution Approach 2:
The patent implements a feedback mechanism where the filtering decision at each sample point depends on the cumulative change history and current rate of change. This feedback loop allows the system to learn from past signal behavior and adjust its filtering aggressiveness accordingly, preventing information loss while maintaining reliability
Data Source
AI summary
Systems and methods for filtering analog signals corresponding to sensed parameters are provided. In this regard, a representative method includes: sampling the analog signal to acquire a sequential series of data points; determining a first cumulative change in value with respect to a first of the data points relative to at least two subsequent data points in the series, the subsequent data points including a second of the data points; determining a second cumulative change in value with respect to the second of the data points relative to at least two data points adjacent to the second of the data points in the series, the at least two adjacent data points including an immediately preceding and an immediately succeeding one of the data points relative to the second of the data points; comparing the first cumulative change and the second cumulative change to respective data thresholds; and outputting a filtered analog signal based, at least in part, on results of the comparing.