Abstract
The traditional blind separation algorithm is mainly for the instantaneous mixing problem in the stable environment. In the practical applications, blind separation often takes into account the interference of the external environment, which requires that the algorithm has strong tracking performance, but the traditional algorithm can’t meet the needs. Aiming at the problem of instantaneous blind separation in non-stationary environment, constrained blind separation algorithm using variable step size and variable momentum factor is proposed in this paper. Based on the nonholonomic natural gradient algorithm, the cost function is constrained by the disturbance of the hybrid system and the constraint factors take the form of self-adaptive adjustment. According to the separation situation, the constraint factors are adjusted adaptively to accelerate the convergence speed. The variable step size based on the cost function gradient is introduced to improve the tracking performance. By incorporating momentum term, the momentum factor is adaptively adjusted to make it have better separation performance. The simulation results show that compared with the traditional algorithm, the proposed algorithm can better balance the contradiction between convergence speed and steady-state error in non-stationary environment, and has better separation performance. In the case of obvious disturbance in the mixed system, the algorithm can effectively improve the shortcomings of the traditional algorithm. In summary, constrained blind separation algorithm using variable step size and variable momentum factor proposed in this paper is effective.
Highlights
Blind source separation (BSS) refers to the recovery of source signals from a set of observed signals only according to the statistical independence of each source signal when the source signal and the mixed system are unknown
Blind separation often takes into account the interference of the external environment, which requires that the algorithm has strong tracking performance, while the traditional algorithm can’t meet the requirements
Aiming at the shortcomings of the above algorithms, constrained blind separation algorithm using variable step size and momentum factor is proposed, which is suitable for non-stationary environments
Summary
Blind source separation (BSS) refers to the recovery of source signals from a set of observed signals only according to the statistical independence of each source signal when the source signal and the mixed system are unknown. Reference [8] based on the separation performance index, the approximate optimal parameters are designed, and proposed the step-size adaptive blind source separation algorithm with adding momentum term. Reference [9, 10], a variable step size blind source separation algorithm with adaptive momentum factor was proposed by using the construction function of the performance evaluation index of the separation signal. Reference [11] proposed an adaptive variable step size algorithm in non-stationary environment by using the disturbance of mixed system, constraint cost function, adaptive constraint factor and step size. Aiming at the shortcomings of the above algorithms, constrained blind separation algorithm using variable step size and momentum factor is proposed, which is suitable for non-stationary environments.
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