Two identification methods of chirp parameters using state space model

We consider the problem of estimating chirp signal parameters. We present two estimation methods based on state space representation of such signals. The first one uses an improvement of Tretter´s approximation to obtain an approximate linear state model. Then the state estimation is performed by Kalman filtering, which is an optimal linear state estimator. The second method is based on an exact nonlinear state model of the signal but the state estimation is issued from extended Kalman filtering, which is an approximate state estimator as it is based on a linearized model. The performance of the two methods is compared by simulation. Finally we extend the second algorithm to multi-component chirp signals, which is impossible for the first method