Parameter estimation in a class of nonlinear state - space models

Many applications rely on a state-space description of the input-output dynamics of the system. In this paper we address the problem of parameter estimation in a class of nonlinear state-space models. The state transition coefficient and the process noise variance are estimated from the received observations in a recursive manner. Some properties of the characteristic function (in this case for normally distributed variables) are exploited. Two applications are presented, estimation of the carrier frequency offset in OFDM and propagation path parameter estimation in channel sounding. Illustrative examples show the reliable performance of the introduced parameter estimation technique