Digitizer Noise Reduction via Cross-Correlation Averaging
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Solution Overview
Problem
Conventional digitizers introduce noise and distortion during signal measurement, particularly for pseudo-periodic time-domain waveforms, limiting the effectiveness of noise reduction techniques like averaging and oversampling, which struggle with signals composed of non-phase-coherent components.
Innovation Solution
The method involves cross-correlating multiple digitized signals from independent channels to separate noise components, averaging these to reduce uncorrelated noise, and combining the resulting amplitude components with a representative phase component to reconstruct an error-reduced waveform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If averaging technique is used to reduce noise in digitized signals, then noise reduction is achieved, but it fails for pseudo-periodic signals with non-phase-coherent components
Solution Approach 1:
The signal is segmented into multiple independent copies that are routed to separate digitizers. Each digitizer processes a portion of the signal independently, allowing subsequent cross-correlation to separate coherent signal components from uncorrelated noise. This segmentation enables noise reduction while preserving pseudo-periodic signal characteristics.
Solution Approach 2:
Cross-correlation is introduced as an intermediary processing step between signal acquisition and averaging. The cross-correlation operation acts as a mediator that identifies and aligns coherent signal components across multiple digitizer outputs while suppressing uncorrelated noise, enabling effective averaging of pseudo-periodic signals.
2Measurement precision
If multiple independent digitizers are used to reduce digitizer error, then error reduction is achieved, but device complexity and cost increase
Solution Approach 1:
Multiple digitizer outputs are merged through cross-correlation and averaging operations. The cross-correlation process combines the independent digitizer measurements in a way that preserves signal integrity while canceling uncorrelated noise and distortion, achieving error reduction without requiring an excessive number of digitizers.
Solution Approach 2:
The cross-correlation operation provides feedback information about the coherence of signal components across different digitizer channels. This feedback mechanism allows the system to identify and reinforce coherent signal components while suppressing incoherent noise, optimizing the error reduction efficiency of the multi-digitizer system.
3Measurement precision
If signal is split into multiple copies for parallel processing, then noise reduction capability is improved, but signal amplitude is reduced
Solution Approach 1:
The signal is copied into multiple independent copies that are processed in parallel through separate digitizers. The cross-correlation and averaging process then reconstructs the signal with reduced noise, effectively creating a high-fidelity copy that compensates for the amplitude reduction caused by signal splitting.
4Measurement precision
If oversampling is used to reduce noise, then noise reduction is achieved, but large sample rates are required for significant error reduction
Solution Approach 1:
The mechanical oversampling approach is replaced with a signal processing substitution using cross-correlation. Instead of relying on high sample rates to achieve noise reduction through averaging, the system uses cross-correlation to identify and reinforce coherent signal components, achieving effective noise reduction at lower sample rates.
Data Source
AI summary
A method and system are provided for reducing noise in a time domain waveform of a signal under test (SUT). The method includes performing cross-correlation of multiple first complex signals and multiple second complex signals, respectively, from the SUT to provide multiple cross-correlated signals, respectively, where the cross-correlated signals have amplitude components and no phase components from the SUT, and where the first and second complex signals include uncorrelated noise, respectively. The method further includes determining an average of the cross-correlated signals to provide an average cross-correlated signal with reduced uncorrelated noise; obtaining a representative phase component from one of the first complex signals or the second complex signals; and combining the representative phase component with the average cross-correlated signal to provide a representative complex signal corresponding to the SUT with reduced uncorrelated noise.