Conjugate gradient method in adaptive bilinear filtering

The application of the conjugate gradient (CG) method for the identification of bilinear systems is investigated. An algorithm based on the CG method is developed for adaptive bilinear digital filtering. In this algorithm, the optimization is done over blocks of input and output data rather than a single pair of data. However, only one iteration and coefficient update is done for every sample of data. This, coupled with the fact that the CG method used does not require a line search makes it very efficient in computation. Simulation results show that this algorithm outperforms the LMS and RLS algorithms in terms of speed of convergence. A preconditioning technique is also applied to further accelerate the convergence in some cases