Compressive-sensing-based sparse signal under-sampling method and implementation device

A sparse signal and compressed sensing technology, applied in the direction of analog-to-digital converters, can solve the problems of wasting sensor time and storage space, without reducing the pressure of front-end ADCs, etc., to facilitate transmission, reduce sampling rate, and reduce speed the effect of the requirements

Active Publication Date: 2013-06-26
HARBIN INST OF TECH
View PDF4 Cites 35 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, with the development of information technology, the signal processing framework based on the Nyquist sampling theorem puts forward higher requirements for the sampling rate and processing speed of the front-end ADC, and also brings huge challenges to the transmission and storage of back-end information. pressure
The common solution to these pressures is signal compression. However, this method of sampling first and then compressing does not reduce the pressure on the front-end ADC, and signal compression means that there is a lot of redundant information in the sampling process, wasting a lot of sensing Resources such as metadata, time, and storage space

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Compressive-sensing-based sparse signal under-sampling method and implementation device
  • Compressive-sensing-based sparse signal under-sampling method and implementation device
  • Compressive-sensing-based sparse signal under-sampling method and implementation device

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0053] The specific embodiment one, the sparse signal undersampling method based on compressed sensing, it is realized by the following steps:

[0054] Step 1, using an m-sequence generator embedded in the FPGA to generate an m-sequence; and using the FPGA to generate a trigger signal synchronously;

[0055] Step 2, using the signal conditioning circuit to perform signal conditioning on the m-sequence generated in step 1, to obtain the conditioned m-sequence;

[0056] Step 3, mixing the conditioned m-sequence obtained in step 2 with the measured sparse signal using a multiplier to obtain a mixed frequency signal;

[0057] Step 4, using a low-pass filter to low-pass filter the mixed frequency signal obtained in step 3 to obtain a low-pass filtered signal;

[0058] Step 5, using the trigger signal generated in step 1 to trigger the sampling circuit, and using the sampling circuit to sample the low-pass filtered signal obtained in step 4 to obtain a sampling result;

[0059] St...

specific Embodiment approach 2

[0060] Specific embodiment two, the implementation device of sparse signal subsampling method based on compressed sensing, it comprises FPGA15, conditioning circuit 16, multiplier 11, low-pass filter 12, sampling circuit 13 and host computer 14;

[0061] Described FPGA15 is embedded with m sequence generator, and described m sequence generator is used for producing m sequence;

[0062] The m sequence output end of described FPGA15 is connected with the signal input end of signal conditioning circuit 16; The signal output end of described signal conditioning circuit 16 is connected with No. 1 signal input end of multiplier 11; No. two signal of described multiplier 11 The input end is used for receiving measured sparse signal; The signal output end of described multiplier 11 is connected with the signal input end of low-pass filter 12; The signal output end of described low-pass filter 12 is connected with the signal input end of sampling circuit 13 Connection; the signal outpu...

specific Embodiment approach 3

[0063] Specific Embodiment 3. The difference between this specific embodiment and the realization device of the compressive sensing-based sparse signal undersampling method described in specific embodiment 2 is that the model of FPGA 15 is EP2C8Q208.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a compressive-sensing-based sparse signal under-sampling method and an implementation device, which are used for lowering a sampling rate of frequency-domain sparse signals on the premise of guaranteeing the restoration effect of the signals. The method comprises the following steps of: generating a trigger signal and an m sequence by an FPGA (Field Programmable Gate Array), carrying out frequency mixing on the m sequence and the measured sparse signal after the m sequence is modulated, and carrying out filtering by a low pass filter; sampling the filtered signal and storing sampled data by a data sampling module after the data sampling module detects the triggering signal; and calculating a transfer function of a system and the m sequence corresponding to the sampled data when the signal is reconstructed, and then restoring the original signal by an OMP (Orthogonal Matching Pursuit) signal reconstructing algorithm. The compressive-sensing-based sparse signal under-sampling method and the implement device are suitable for the under-sampling of frequency-domain sparse analogue signals.

Description

technical field [0001] The invention relates to a sparse signal under-sampling method and a realization device. Background technique [0002] The traditional information sampling process must follow the Nyquist sampling theorem, that is, the sampling rate must be at least twice the highest frequency of the original signal, so that the original signal can be recovered from the discrete data obtained by sampling without distortion. However, with the development of information technology, the signal processing framework based on the Nyquist sampling theorem puts forward higher requirements for the sampling rate and processing speed of the front-end ADC, and also brings huge challenges to the transmission and storage of back-end information. pressure. The common solution to these pressures is signal compression. However, this method of sampling first and then compressing does not reduce the pressure on the front-end ADC, and signal compression means that there is a lot of redun...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H03M1/12
Inventor 张京超付宁乔立岩宋平凡
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products