Adaptive equalization via Kalman filtering techniques

In this paper the arithmetic complexity and the mean squared error (MSE) performance of three adaptive equaliser structures are compared. The first is a conventional decision feed-back equaliser (DFE) which utilises a Godard-Kalman adaptive algorithm to carry out the tap weight update. The second is an adaptive Kalman equaliser which utilises a least mean squares (LMS) algorithm to carry out the channel estimation process and a Kalman filter structure for the data estimation. The final, novel, structure considered utilises the performance advantage of both of the previous structures. This is achieved by using the basic structure of the adaptive Kalman equaliser but incorporating an element of decision feedback.