Detection and estimation of multiple cisoids in colored noise by Bayesian predictive densities

A new criterion based on Bayesian predictive densities and subspace decomposition is proposed to estimate the number and the frequencies of close cisoids in colored noise. The colored noise is modeled by an autoregression whose order has also to be estimated. The proposed criterion significantly outperforms the MDL and AIC in correctly determining the number of cisoids and the order of the autoregressive process. Furthermore, an algorithm for frequency estimation is proposed that considerably reduces the computational complexity of the criterion.