Estimation of chirp radar signals in compound-Gaussian clutter: acyclostationary approach
IEEE Transactions on Signal Processing
Maximum likelihood parameter estimation of superimposed chirpsusing Monte Carlo importance sampling
IEEE Transactions on Signal Processing
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Chirp signals play an important role in the statistical signal processing. Recently Kundu and Nandi (2008) [8] derived the asymptotic properties of the least squares estimators of the unknown parameters of the chirp signals model in the presence of stationary noise. Unfortunately they did not discuss any estimation procedures. In this article we propose a computationally efficient algorithm for estimating different parameters of a chirp signal in presence of stationary noise. From proper initial guesses, the proposed algorithm produces efficient estimators in a fixed number of iterations. We also suggest how to obtain the proper initial guesses. The proposed estimators are consistent and asymptotically equivalent to least squares estimators of the corresponding parameters. We perform some simulation experiments to see the effectiveness of the proposed method, and it is observed that the proposed estimators perform very well. For illustrative purposes, we have performed the data analysis of a simulated data set. Finally, we propose some generalization in the conclusions.