Signal Autocorrelation Modulation Compression Sampling and Restoration Method

Abstract

The invention discloses a signal self-correlation modulated compressed sampling and restoring method. The method is mainly used for solving the problem in the prior art that a demand for a signal sampling rate is too high. The method comprises following steps of (1), carrying out self-correlation modulation and self-correlation modulated signal sampling to input analog signals; (2), carrying out low pass filtering and low speed sampling to the modulated signals, thus acquiring compressed sampling signals; (3), constructing an observing matrix by utilizing sampled self-correlation modulated signals; (4), optimally solving a sparse vector by utilizing the compressed sampling signals and the observing matrix according to a compressed sensing theory; and (5), restoring original signals according to the solved sparse vector. According to the method, the signal sampling rate can be reduced greatly; the signal restoring accuracy is ensured; and the method can be used for low speed acquisition of broadband signals.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a signal autocorrelation modulation compression sampling method based on compressed sensing, which can be used for low-speed acquisition of ultra-wideband signals. Background technique [0002] With the development of modern technology, people's demand for information is increasing. According to the Nyquist sampling theorem, the requirements for signal sampling are getting higher and higher, but high-frequency signal sampling increases the difficulty of sampling system design, and the discretization The large amount of data generated in the end also puts great pressure on storage and transmission processing. Therefore, hardware devices with larger storage capacity and computers with super high computing power are required to process these data, but these hardware resources are limited. [0003] In order to solve the above problems, the compressed sensing theory emergin...

AI Technical Summary

Problems solved by technology

Too low a conversion rate will greatly reduce the quality of signal reconstruction; too high a conversion rate will bring great difficulties to the hardware; therefore, this brings difficulties to the practical application of the sampling structure

On the other hand, the random modulation method does not optimally preserve the energy of the signal. Although the information of the signal is not lost, the number of samples required to reconstruct the signal when the signal-to-noise ratio is low will greatly increase.

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