Abstract
To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.
Highlights
To reduce professional equipment installation for intelligent communication, realize better information transmission with limited resources, and receive a large amount of source information using limited sensors at the receiving end, use of a low-element model at the receiving ends is becoming popular in the field of communication [1,2,3]
As blind-source separation aims to address the problems of nonlinear signals and the time-frequency analysis uses the non-stationarity of signals, this paper begins by discussing time-frequency analytical techniques for signal separation
This paper proposes low-element image restoration based on an out-of-order elimination algorithm
Summary
To reduce professional equipment installation for intelligent communication, realize better information transmission with limited resources, and receive a large amount of source information using limited sensors at the receiving end, use of a low-element model at the receiving ends is becoming popular in the field of communication [1,2,3]. Blind-source separation (BSS) involves separating the best estimation of the hidden source signals from certain observed signals (at the receiving end) when the theoretical model of the signal and Entropy 2019, 21, 1192; doi:10.3390/e21121192 www.mdpi.com/journal/entropy. Underdetermined blind-source separation is a low-element model of sensors at the receiving end for signal processing, which remains an ill-posed and abstruse problem for information transmission [15,16,17,18]. As blind-source separation aims to address the problems of nonlinear signals and the time-frequency analysis uses the non-stationarity of signals, this paper begins by discussing time-frequency analytical techniques for signal separation
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