The problem of identifying digital transmitted symbols over nonlinear communication channels is addressed. The equalization scenario is considered from the decision point of view, and constitutes a joint identification and estimation situation due to incomplete knowledge of the system model. A new class of multilinearization algorithms for nonlinear systems is derived according to partitioning theory concepts. The procedure targets on adaptively selecting the best reference points for linearization from an ensemble of generated trajectories that span the whole state space of the desired signal. In the various simulations examined, the partitioned-based equalizer is found superior to the classical extended Kalman filter.

The problem of estimating the return power in a LIDAR system in the presence of multiplicative noise (speckle) is addressed. A significant class of the partitioning approach is applied and comparisons are made with the extended Kalman filter (EKF) in the case where model parameter uncertainty exists. Through extensive simulations, the partitioned filter is shown to be significantly superior to the EKF algorithm.