Signal separation processor based on second-order statistic algorithms
Tertulien Ndjountche, Rolf Unbehauen, Fa-Long Luo · 2003
A processor architecture for the separation of mixed signals is proposed. It consists of a demixing circuit which is based on the structure of a finite impulse response (FIR) filter and a learning circuit which updates the FIR variable coefficients according to a stochastic gradient algorithm with variable step size in order to minimize the cost function defined as the cross-correlation of the output signals. Using theoretical predictions and simulations, the components used in the building blocks were sized to meet the precision requirement of 12 bits. As a result, the convergence of the resulting structure is fast and reliable.