A fast recursive algorithm for the maximum likelihood estimation of the parameters of a periodic signal
Describes a new recursive algorithm for the estimation of the parameters of a periodic signal in additive Gaussian white noise. These parameters are the period, and the complex amplitudes of the harmonics present. The proposed algorithm is based on recursive maximum likelihood (ML) algorithms for incomplete data as described by Titterington (1985) and others. These algorithms are of complexity O(NM), where N is the number of harmonics, and M is the signal length. The performance of the method is compared to that of the extended Kalman filter with the aid of simulations. >