Analog-to-Information Conversion for Low-Rate Wideband Sampling
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Solution Overview
Problem
Current analog-to-digital converter (ADC) technologies face limitations in high-speed, high-resolution sampling, particularly in wideband applications such as radar and signals intelligence, where they struggle to differentiate weak signals from clutter and noise, and are hindered by incremental development pace and high data rates that overwhelm back-end digital signal processing algorithms.
Innovation Solution
The development of a new paradigm for analog-to-information conversion (AIC) that uses compressed sensing and streaming algorithms to extract relevant information from sparse signals through nonlinear processes, replacing uniform time samples with general linear functionals, allowing for reduced sampling rates and focused data acquisition on significant signal components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional ADC technologies are used for wideband signal acquisition, then sampling bandwidth can be increased, but the ability to differentiate weak signals from clutter and noise deteriorates
Solution Approach 1:
The patent extracts only the essential information from wideband signals by using random projection matrices to capture dominant signal components while discarding redundant data. This extraction approach enables weak signal detection by focusing computational resources on the most significant signal features rather than processing all sampled data uniformly.
Solution Approach 2:
The patent transforms the sampling approach by changing from uniform time-domain sampling to random linear projections in a transformed domain. This parameter change in the sampling methodology allows the system to achieve both wideband coverage and enhanced weak signal differentiation through compressed sensing reconstruction algorithms.
2Measurement precision
If ADC sampling rate and resolution are increased to capture more signal detail, then measurement precision is improved, but data rate increases overwhelming back-end DSP algorithms
Solution Approach 1:
The patent extracts only the critical information needed for signal reconstruction by using random projections that capture the essential signal components. This extraction reduces the data volume to be processed while maintaining reconstruction accuracy, thereby preventing backend DSP algorithms from being overwhelmed by excessive data rates.
Solution Approach 2:
The patent applies partial sampling by capturing only the minimum necessary measurements required for accurate signal reconstruction through compressed sensing. This partial action approach collects sufficient data for high-fidelity reconstruction without generating the deluge of data that would overwhelm processing systems.
3Manufacturing precision
If ADC development follows incremental improvements based on Nyquist/Shannon sampling theory, then manufacturing precision is maintained, but the pace of development becomes insufficient for pressing applications
Solution Approach 1:
The patent fundamentally changes the sampling paradigm from Nyquist-rate uniform sampling to compressed sensing with random projections. This parameter change in the sampling theory enables跨越式 development by allowing accurate signal acquisition at rates below the traditional Nyquist limit, thereby accelerating development pace without sacrificing precision.
Solution Approach 2:
The patent substitutes the mechanical constraint of fixed Nyquist sampling rates with a more flexible mathematical framework based on random projections and sparsity exploitation. This substitution replaces the rigid sampling mechanism with an adaptive system that can achieve high performance with fewer measurements, enabling faster development iterations.
4Ease of operation
If uniform time samples are used for signal acquisition, then ease of operation is maintained, but the system cannot focus on significant signal components efficiently
Solution Approach 1:
The patent performs preliminary random projection transformations on the sampled data to organize information in a way that facilitates efficient extraction of significant signal components. This preliminary action prepares the data structure to enable focused analysis on dominant features while maintaining operational simplicity through standardized processing pipelines.
Data Source
AI summary
A typical data acquisition system takes periodic samples of a signal, image, or other data, often at the so-called Nyquist/Shannon sampling rate of two times the data bandwidth in order to ensure that no information is lost. In applications involving wideband signals, the Nyquist/Shannon sampling rate is very high, even though the signals may have a simple underlying structure. Recent developments in mathematics and signal processing have uncovered a solution to this Nyquist/Shannon sampling rate bottlenck for signals that are sparse or compressible in some representation. We demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals. One embodiment of our invention is a low-rate analog-to-information converter that can replace the high-rate analog-to-digital converter in certain applications involving wideband signals. Another embodiment is an encoding scheme for wideband discrete-time signals that condenses their information content.