Maximum likelihood, ESPRIT, and periodogram frequency estimation of radar signals in K-distributed clutter. (Q1575366)

The contribution of this paper is the derivation of the joint maximum likelihood (ML) estimator of complex amplitude and Doppler frequency of a radar target signal embedded in correlated non-Gaussian clutter modelled as a compound-Gaussian process. The estimation accuracy of the ML frequency estimator is investigated and compared with that of the well-known periodogram and ESPRIT estimators under various operational scenarios. The hybrid Cramér-Rao lower bound and a large sample closed-form expression for the mean square estimation error are also derived for the Swerling I target signal. Finally, numerical results obtained by Monte Carlo simulation are checked by means of measured sea clutter data for the general case of fluctuating target amplitude.