A new class of efficient adaptive nonlinear filters (ANLF)

Abstract

A multilinearization procedure is described, with the use of which a new class of algorithms for nonlinear filtering can be realized. The methodology 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. Through simulations, the approach is shown to be significantly superior to the classical extended Kalman filter and comparable in computational burden.