Adaptive nonlinear digital filter with sequential regression algorithm

The purpose of this paper is to introduce an adaptive nonlinear digital filtering algorithm which use the sequential regression (SER) method to update the second order Volterra filter coefficients in a recursive way. Conventionally, the SER method has been used to invert large matrices which result from direct application of the Wiener filter large matrices which result from direct application of the Wiener filter theory to the Volterra filter. However, the algorithm proposed in this paper adopts the simplified least squares solution that results when the input signals are Gaussian, and hence the size of the matrix to be inverted in smaller than that of the conventional approaches. Simulation results are also included to demonstrate the performance of the proposed algorithm.