A globally convergent adaptive IIR filter

Abstract

An open issue in Adaptive IIR Filtering (AIF) is that of convergence to a global minimum in the presence of observation noise, when the system is insufficiently modeled, or when the excitation source is colored. It is well known that algorithms based on Equation Error (EE) contain a single minimum that may be biased whereas, algorithms based on Output Error (OE) ensure the existence of an unbiased global minimum in presence of local minima. Recently, there have been a number of attempts to combine these formulations in order to ensure the existence and uniqueness of an unbiased minimum. The work presented here, Equation Error Output Error (EEOE) and Modified EEOE (MEEOE,) are such attempts in the context of system identification. Although the formulation of EEOE did not achieve the desired outcome and was later found out to be similar to that proposed by Kenny and Rohrs (1993), the exploration of its limitations, however, led to a superior algorithm namely, MEEOE.