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

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

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.

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.