Fractional Decimation Sampling for Waveform Phase Tracking
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Solution Overview
Problem
Existing digital processing methods for waveform data face challenges in efficiently decimating over-sampled waveforms, particularly in identifying the optimal samples to retain for reconstructing the original waveform, which affects data rate conversion and phase tracking.
Innovation Solution
The method involves identifying samples closest to a selected time within each data period, using a fractional decimation process that selects samples based on their offset from the selected time, allowing for the reduction of over-sampled data to a lower data rate while tracking phase and frequency variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If over-sampled waveform data is retained to improve waveform reconstruction accuracy, then measurement precision is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The patent extracts only the necessary samples from over-sampled waveform data by identifying samples whose fractional time positions match target fractional positions. This extraction process removes redundant samples while preserving the essential waveform information needed for accurate reconstruction, thereby reducing data processing complexity without sacrificing measurement precision.
Solution Approach 2:
The patent applies local quality by treating different samples differently based on their fractional time positions. Samples at specific fractional positions are selected for retention while others are discarded. This selective approach ensures that only the most relevant samples are processed and stored, optimizing both accuracy and computational efficiency.
2Measurement precision
If over-sampled waveform data is retained to improve waveform reconstruction accuracy, then measurement precision is improved, but data volume increases
Solution Approach 1:
The patent extracts only the necessary samples from over-sampled waveform data by identifying samples whose fractional time positions match target fractional positions. This extraction process removes redundant samples while preserving the essential waveform information needed for accurate reconstruction, thereby reducing data volume without sacrificing measurement precision.
3Ease of operation
If a fixed sample rate is used to simplify sampling, then ease of operation is improved, but adaptability to varying waveform frequencies decreases
Solution Approach 1:
The patent introduces dynamic elements into the fixed sample rate system by calculating fractional time positions and selecting samples based on these dynamic fractional positions. Although the sample rate itself remains fixed, the selection process adapts to varying waveform frequencies by identifying samples at appropriate fractional positions within each data period, thereby maintaining ease of operation while improving adaptability.
Solution Approach 2:
The patent changes the parameter of sample selection from fixed time intervals to fractional time positions. By selecting samples based on their fractional position within a data period rather than fixed time intervals, the system maintains a fixed sample rate for operational simplicity while adapting to different waveform frequencies through the fractional position calculation.
Data Source
AI summary
Methods for processing waveforms may include decimating an over-sampled waveform by identifying samples for which the sample's position within a data period indicates that is closest to a selected time within a data period. In some example applications, the selected time may be determined as a preferred time to sample the waveform within a data period. In an illustrative example, a sequence of samples representing an over-sampled waveform may be reduced by identifying a sample in each data period that is closest in time to the selected time. In another illustrative example, a sample within each data period may be identified if it falls within a range that is a function of the selected time within the data period and an integral ratio of a sample period to the data period. The identified samples may be used to reconstruct the original waveform with fewer samples than the over-sampled waveform.