Noise Analyzer Cross-Correlation Metric for Measurement Time Optimization
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Solution Overview
Problem
Existing methods for measuring power density spectra using cross-correlation struggle with determining the optimal number of averagings, leading to inefficiencies in measurement time and lack of feedback on the suppression of uncorrelated noise components.
Innovation Solution
A method and device that calculate a special metric from Fourier-transformed signals to automatically determine the number of averagings needed, allowing for termination of the measurement when further averaging does not significantly improve accuracy, thereby optimizing measurement time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of averagings is increased to improve measurement precision, then the measurement time increases proportionally
Solution Approach 1:
The patent implements a feedback mechanism by calculating a metric from the Fourier-transformed signals of two paths and using it to automatically determine when to terminate the averaging process. This feedback loop allows the system to stop averaging when the metric indicates sufficient precision has been achieved, rather than requiring a predetermined fixed number of averagings, thus optimizing measurement time while maintaining precision.
2Measurement precision
If cross-correlation averaging is performed to suppress uncorrelated noise, then measurement precision improves, but measurement time increases
Solution Approach 1:
The system calculates a metric based on the Fourier-transformed signals from two paths and uses this metric as feedback to determine when to terminate the cross-correlation averaging process. This allows automatic optimization of the averaging duration, stopping when the metric indicates that further averaging provides diminishing returns, thus reducing measurement time while maintaining effective noise suppression.
3Measurement precision
If a fixed number of averagings is predetermined, then device complexity is reduced, but measurement precision cannot be optimized
Solution Approach 1:
The system performs self-service by automatically calculating the metric from its own measurement signals and using this metric to determine the optimal termination point for averaging. The device evaluates its own measurement quality in real-time and makes autonomous decisions about when to stop averaging, eliminating the need for external intervention or complex predetermined configurations while achieving optimized precision.
Data Source
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AI summary
To measure the power density spectrum of an input signal, Fourier transformation signals are formed in two paths. A number of averages are used for cross correlation of the two signals. A value (Metrik L) is computed from the two signals and the averages as the exit point criterion for further averaging. The computing unit (33) takes the two signal values ((y1(k),y2(k)) to give their squares (20,21) to be given mean values (22,23). The signals are also multiplied (26) to be given a mean value (27). A divider (25) is linked to a multiplier (29) with the number (N) of averages to give the value.