Momentum term technology is an effective solution to improve the performance of the adaptive blind source separation (BSS) algorithm

but the convergence property of the momentum term based BSS algorithm is very sensitive to the fixed momentum factor

and its performance in steady state is also restricted by the step size. Firstly

the principle of the momentum term based BSS algorithm as well as its two disadvantages were presented and analyzed in this paper. Then

in order to eliminate the first disadvantage of the momentum term based algorithm using the fixed momentum factor

we structured a variable momentum factor algorithm with the adaptive adjustment property based on the gradient descent method. On this basis

by virtue of the convex combination theory

an adaptive combination of tow variable momentum factor algorithms with different step size was proposed to alleviate the performance restriction caused by the step size. The simulation results in different conditions demonstrate that the proposed modified strategies got the optimization balance between the fast convergence speed and small steady-state error

and effectively avoid the two drawbacks of the momentum term based BSS algorithm.