Method for reconstructing signals

Abstract

The invention belongs to the technical field of wireless communications, and particularly relates to a method for reconstructing signals in an ultra-wide-band communication system by the adoption of a low-complexity improved Bayes compressed sensing algorithm. The method comprises the steps that firstly, sectioning and filtering are conducted on received signals, then a measuring matrix is used for conducting linear combination on the filtered signals again, after a series of relatively simple iterative operations, the expansion coefficient of original signals under the characteristic base can be measured, and therefore reconstruction of the original signals is realized. According to the method for reconstructing the signals, on one hand, the Bayes compressed sensing algorithm is improved by fully utilizing the characteristics of the characteristic base, the restorability is improved, more importantly, the complex matrix inversion process in the Bayes compressed sensing algorithm is avoided, and particularly, when the length of the signals is large and the order of the matrix is large, the signal restoration operation complexity can be effectively lowered.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for reconstructing a signal by using a low-complexity improved Bayesian compressed sensing algorithm in an ultra-wideband communication system. Background technique [0002] Since UWB (Ultra Wideband) signals are mostly narrow pulse signals in the time domain, their frequency domain bandwidth is too wide. According to the requirements of the Nyquist sampling law, the sampling rate will be as high as several GHz, so that the analog-to-digital converter The design brings huge challenges, and a large amount of data processing brings a great burden to the digital signal processor, the current hardware level is difficult to achieve the above requirements. Compressed sensing (CS, Compressed sensing), as a novel low-speed signal sampling theory, breaks through the limitation of Nyquist sampling law. Some literatures have carried out theoretical resear...

AI Technical Summary

Problems solved by technology

[0007] The algorithm using eigenbasis combined with Bayesian compressed sensing needs to invert the matrix. In particular, the eigenexpansion basis needs to use the statistical characteristics of the signal to improve the recovery ability of the signal. The length of the signal is relatively long, and the order of the matrix will be Relatively large, the inversion operation will greatly increase the complexity of the operation, making it very difficult to apply and promote. Therefore, it is an innovative and important practical significance to study a low-complexity improved Bayesian compressed sensing algorithm. challenging task

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