Test Instrument Noise-Induced Signal Drift Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern test instruments, such as oscilloscopes, face the challenge of noise-induced signal drift when integrating input signals over time, leading to divergent integrals that do not comply with the original waveform due to accumulated noise, particularly 1/f noise, which is difficult to filter automatically without prior knowledge of the input signal.
Innovation Solution
A method and test instrument that determine the antiderivative of the input signal by optimizing both the derivative and absolute deviation, using an optimization problem to balance the shape of the waveform and minimize divergence from zero, thereby counteracting noise introduced by the instrument's frontend, without requiring prior knowledge of the signal or manual selection of noise frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the input signal is integrated over time to obtain non-supervised variables, then the ability to measure indirect variables is improved, but noise accumulates causing the integral to diverge from zero
Solution Approach 1:
The patent implements feedback by continuously monitoring the integral value and comparing it against the expected behavior (antiderivative of waveform). When noise causes deviation from zero, the system adjusts the integration process to correct the drift, ensuring the integral remains reliable over time while still capturing valid non-supervised variables.
Solution Approach 2:
The patent changes the integration parameter by introducing a drift correction mechanism that dynamically adjusts the integral calculation. Instead of simple accumulation, the system modifies the integration parameter to compensate for noise-induced drift, allowing accurate measurement of non-supervised variables without divergence.
2Measurement precision
If noise filtering is applied in the frequency spectrum, then noise reduction is improved, but automatic filtering becomes critical and manual selection requires prior knowledge of the input signal
Solution Approach 1:
The patent applies self-service by enabling the system to automatically identify and correct noise-induced drift without requiring manual intervention or prior knowledge of the input signal. The drift correction mechanism autonomously monitors and adjusts the integral, making the filtering process self-regulating and eliminating the need for complex manual frequency selection.
Solution Approach 2:
The patent extracts the noise component from the integral by separating the drift correction function from the main integration process. By isolating and specifically targeting the noise-induced drift component, the system achieves effective noise reduction without requiring complex full-spectrum filtering or manual frequency analysis.
3Ease of manufacture
If simple integration of the waveform is performed, then the calculation process is simplified, but the integral diverges from zero due to accumulated noise
Solution Approach 1:
The patent applies preliminary action by preparing a drift correction mechanism before the integration process begins. The system pre-establishes the correction algorithm and monitoring parameters, so that when integration occurs, the noise-induced drift is automatically compensated in real-time, maintaining both simplicity and accuracy without requiring complex post-processing.
Data Source
AI summary
The present disclosure relates to a method of reducing a noise induced signal drift. The method comprises: receiving an input signal; recording a waveform of the input signal; and determining an antiderivative of the waveform by optimizing a derivative of the antiderivative to be determined and an absolute deviation of the antiderivative to be determined. Further, the present disclosure relates to a test instrument for analyzing an input signal.