Signal Resampling with MASH DDSM for Smooth Sample-Rate Changes
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Solution Overview
Problem
Existing signal processing techniques face challenges in efficiently converting sample rates without losing information, particularly when the sampling periods of different devices are mismatched, leading to high computational and memory requirements, complex hardware implementations, and inability to smoothly change sampling rates over time.
Innovation Solution
A system utilizing a shaping filter with a dual-modulus counter and Multi-stAge noise Shaping Digital Delta-Sigma Modulator (MASH DDSM) to control sampling rates, allowing for smooth changes in sampling rates and efficient resampling of signal data by using a windowed-sinc filter and error feedback modulators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rational resampling with digital lowpass filter is used, then sample rate conversion can be performed, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The resampling process is divided into two independent stages: an interpolation stage that up-samples the signal by factor L, and a decimation stage that down-samples by factor M. Each stage uses its own simple filter (interpolation filter and decimation filter respectively) rather than one complex filter, reducing overall computational complexity while maintaining conversion accuracy.
Solution Approach 2:
The digital lowpass filter is extracted and replaced with a specialized interpolation filter designed specifically for the interpolation stage. This filter uses a fixed, simple structure with predetermined coefficients that are optimized for the specific interpolation ratio, eliminating the need for complex adaptive filtering and reducing memory requirements for coefficient storage.
2Productivity
If polyphase implementations are used to reduce computational effort, then processing speed improves, but coefficient storage and processing requirements increase
Solution Approach 1:
The interpolation filter uses different coefficients for different phases of the interpolation process, where each phase has locally optimized coefficients tailored to its specific function. This allows each phase to use simpler, smaller coefficient sets rather than storing all possible coefficients, reducing overall memory requirements while maintaining processing speed through specialized local optimizations.
3Adaptability or versatility
If programmable L/M ratio is implemented, then flexibility in resampling ratios is improved, but hardware implementation complexity increases
Solution Approach 1:
The system achieves flexible resampling ratios through dynamic control of the interpolation and decimation factors L and M. Rather than using complex programmable filters, the flexibility is achieved by dynamically adjusting the sampling rates at which data is read from and written to the buffer, allowing any rational resampling ratio to be implemented with simple, fixed-structure filters.
4Adaptability or versatility
If classical resampling technique is used, then conversion by any rational factor is possible, but sampling rates cannot be changed smoothly over time
Solution Approach 1:
The system performs preliminary interpolation to create an up-sampled signal before decimation, using a buffer to store intermediate values. This preliminary action allows the system to prepare for smooth transitions between different sampling rates by pre-computing interpolation values, enabling continuous and smooth sampling rate changes without abrupt discontinuities.
Data Source
AI summary
An instrument configured to process signal data is disclosed. The instrument is operable to control and or change the sampling rate of the signal data from a first sample rate to a second sample rate different than the first sample rate.