Sub-Nyquist Signal Compression With Zone Detection and Phase Compensation
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Solution Overview
Problem
Conventional compressed sensing techniques face a significant computational load in optimizing the basis and component values, making processing of broadband signals impractical, especially at rates of 1 GS/s or more.
Innovation Solution
A data compression apparatus that utilizes an MCS receiver to add different delay times to signals, samples them at a sub-Nyquist rate, and performs phase compensation and frequency domain conversion to determine the sub-Nyquist zone of the target signal, reducing computational load while improving compression ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compressed sensing technique is used to represent sparse signal with fewer variables, then compression ratio is improved, but computational load increases significantly
Solution Approach 1:
The patent segments the frequency spectrum into multiple sub-Nyquist zones and processes signals in each zone separately. By dividing the broadband signal into narrower frequency bands, the optimization processing complexity is reduced while maintaining compression capability for sparse signals
Solution Approach 2:
The patent performs preliminary frequency domain conversion and sub-Nyquist zone determination before optimization processing. By pre-processing the signal to identify which sub-Nyquist zones contain target signals, the subsequent optimization processing only needs to search within limited zones rather than the entire frequency range, significantly reducing computational load
2Measurement precision
If optimization processing is performed to search for proper basis and component values, then sparsity representation accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies different processing strategies to different sub-Nyquist zones based on whether they contain target signals. Zones with target signals undergo full optimization processing for accurate sparsity representation, while zones without signals are quickly identified and skipped, achieving local optimization of processing accuracy and speed
Solution Approach 2:
The patent performs preliminary frequency domain conversion and determines which sub-Nyquist zones contain target signals before executing optimization processing. This preliminary identification allows the system to limit the optimization search to only those zones containing signals, maintaining accuracy where needed while improving overall processing speed
Data Source
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AI summary
An MCS receiver (120) that outputs sampling sequences corresponding to signals obtained by adding different delay times to different signals obtained by branching a target signal into a plurality of lines, and sampling the signals at a sampling rate less than the Nyquist rate, and an MCS encoder (130) that converts the sampling sequences into compressed data are included. The MCS encoder (130) includes a sub-FFT (131) that converts the sampling sequences into frequency-domain signals, a signal processing unit (132) that performs, at one time, phase compensation processing for sub-Nyquist zones of the sampling sequences, and processing to cancel phase rotation due to delay time differences between the sampling sequences, a target frequency estimator (133) that determines into which sub-Nyquist zone the target signal has been folded and estimates the frequency of the target signal, and an encoding unit (134) that converts a value representing the sub-Nyquist zone and the corresponding amplitude value into the compressed data and output the compressed data.