A New Algorithm to Estimate Mixing-Matrix of Underdetermined Blind Signal Separation

2012 ◽  
Author(s):  
Cui Zhi-Tao ◽  
Jian Ke
Keyword(s):  
Signal Separation ◽  
Blind Signal Separation ◽  
Blind Signal ◽  
Mixing Matrix ◽  
Underdetermined Blind Signal Separation

A Parallel Dual Matrix Method for Blind Signal Separation

2014 ◽  
Vol 26 (3) ◽  
pp. 592-610
Author(s):  
T. J. Zeng ◽  
Q. Y. Feng
Keyword(s):  
Cost Function ◽  
Computer Simulations ◽  
Matrix Method ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Search Region ◽  
Blind Signal ◽  
Numerical Relation ◽  
Mixing Matrix

A parallel dual matrix method that considers all cases of numerical relations between a mixing matrix and a separating matrix is proposed in this letter. Different constrained terms are used to construct cost function for every subalgorithm. These constrained terms reflect numerical relation. Therefore, a number of undesired solutions are excluded, the search region is reduced, and the convergence efficiency of the algorithm is ultimately improved. Moreover, any parallel subalgorithm is proven to converge to a desired separating matrix only if its cost function converges to zero. Computer simulations indicate that the algorithm efficiently performs blind signal separation.

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.

scholarly journals Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4233
Author(s):  
Zheng Dai ◽  
Weimin Su ◽  
Hong Gu
Keyword(s):  
Linear Array ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Observation Data ◽  
Maximum Point ◽  
Uniform Linear Array ◽  
Error Matrix ◽  
Blind Signal ◽  
Phase Errors ◽  
Mixing Matrix

In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm.

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