scholarly journals Optimization of Kurtosis in the Extend-Infomax Blind Signal Separation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhitao Cui ◽  
Yongcai Zhang ◽  
Niu Yi
Keyword(s):  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Control Variable ◽  
Optimization Method ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Separation Algorithm ◽  
Blind Signal ◽  
Source Signal ◽  
Number Of Iterations

A kurtosis optimization method is proposed to improve the blind separated signal qualities based on the extend-infomax algorithm. The kurtosis of the hypothetical source signal was optimized based on the probability density function of sub-Gaussian signals. Obtained parameters after kurtosis optimization were then utilized to validate the effectiveness of the algorithm, which showed that the running time of the algorithm was significantly reduced, and the qualities of the separated signals were enhanced. Methods. Using kurtosis as a control variable, a one-way analysis of variance (ANOVA) was carried out on the algorithm’s performance metrics, the number of iterations, and the signal-to-noise ratio of the separated signal. Results. The results showed that there were significant differences in the above metrics under different kurtosis levels. The curves of average metric values indicate that, with the increase in kurtosis of the hypothetical source signal, the performance of the algorithm was improved.

APPLICATION OF BLIND SIGNAL SEPARATION TECHNIQUES FOR DETECTION OF PHASE-SHIFTED RADIO SIGNALS

2021 ◽  
Author(s):  
Н.Ю. ЛИБЕРОВСКИЙ ◽  
Д.С. ЧИРОВ ◽  
Н.Д. ПЕТРОВ
Keyword(s):  
Radio Signal ◽  
Signal To Noise Ratio ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Signal To Noise ◽  
Separation Algorithm ◽  
Blind Signal ◽  
Radio Signals ◽  
Joint Cumulants ◽  
Mixing Matrix

Целью данной работы является исследование эффективности алгоритма слепого разделения сигналов (СРСв задаче обнаружения цифровых фазоманипулированных радиосигналов. Рассмотрены классические методы СРС и критерии независимости сигналов. Исследована модель алгоритма СРС, основанного на вычислении размешивающей матрицы, которая приводит совместные кумулянты второго и четвертого порядков к нулю. Для исключения тривиального решения накладываются дополнительные ограничения на дисперсии сигналов. Приводится система уравнений для нахождения коэффициентов размешивающей матрицы. Показан вид коэффициентов размешивающей матрицы, приводящей сигналы к некоррелированному виду. Доказана возможность аналитического решения уравнения, связанного с равенством совместного кумулянта четвертого порядка к нулю. По результатам моделирования алгоритма СРС показано, что предложенный алгоритм позволяет обеспечить прием ФМ-2 радиосигнала на фоне гауссовой помехи. Выигрыш в отношении сигнал-помеха составляет не менее 2 дБ. The purpose of this work is to study the effectiveness of the blind signal separation algorithm in the problem of detecting digital PSK radio signals. Classical methods of blind signal separation and criteria of signal independence are considered. A model of a blind signal separation algorithm based on the calculation of a mixing matrix that reduces the joint cumulants of the second and fourth orders to zero is investigated. To eliminate the trivial solution, additional restrictions are imposed on the signal variances. A system of equations for finding the coefficients of the mixing matrix is given. The view of the coefficients of the mixing matrix, which leads the signals to an uncorrelated form, is shown. The possibility of an analytical solution of the equation associated with the equality of the joint cumulant of the fourth order to zero is proved. Based on the results of the simulation of the blind signal separation algorithm, it is shown that the proposed algorithm allows receiving the PSK-2 radio signal against the background of Gaussian interference. The gain in the signal-to-noise ratio is at least 2 dB.

Optimizing the Algorithm of FECG Separation from MECG Based on ICA Rationale

2013 ◽  
Vol 846-847 ◽  
pp. 1257-1261
Author(s):  
Heng Yan Zhou ◽  
Yu Cong Xu ◽  
Yu Xi Luo ◽  
Yu Bao Gao
Keyword(s):  
Rate Of Convergence ◽  
Signal To Noise Ratio ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Signal To Noise ◽  
Persistent Problem ◽  
Blind Signal ◽  
Fetal Ecg ◽  
Fast Independent Component Analysis ◽  
Ica Algorithm

The study presents a method to separate the fetal electrocardiograph (FECG) from concomitant maternal electrocardiograph (MECG) by using Fast Independent component analysis (ICA) algorithm of Blind Signal Separation. Current methods of extracting fetal ECG have defects and drawbacks. Traditional ICA method has a persistent problem that the signal of FECG extracted from MECG was always mixed with the signal of MECG in diverse levels, and the order of MECG and FECG is uncertain, resulting in the decrease of its rate of convergence. To improve the rate of convergence, this research adopts Fast ICA algorithm. Experimental results indicate that this method is useful for extracting the fetal signal of ECG. And a satisfactory signal to noise ratio (SNR) is obtained.

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