Estimation of Chirp Signals in Gaussian Noise by Kalman Filtering

The paper addresses the problem of estimating the chirp signals embedded in Gaussian noise. The proposed method is based on a model of the signal phase as a polynomial. This approach offers the opportunity to represent these signals by an adequate state space model and to apply standard Kalman filtering procedures in view to estimate the parameters of chirp signals. Procedure simulations were made on linear chirp sinusoids with time-varying amplitude and are consistent with the theoretical approach. The paper presents the most important results.