Conjugate gradient techniques for adaptive filtering

The application of the conjugate gradient technique for the solution of the adaptive filtering problem is discussed. An algorithm that does not require a line search or a knowledge of the Hessian is developed based on the conjugate gradient method. The choice of the gradient average window in the algorithm is shown to provide a trade-off between computational complexity and convergence performance. The method is capable of providing convergence comparable to recursive least squares (RLS) schemes at a computational complexity that is intermediate between the least mean square (LMS) and the RLS methods and does not suffer from any known instability problems