A linear prediction approach to Doppler mean frequency retrieval in the presence of ground clutter

Linear-prediction-based methods are applied to mean Doppler frequency estimation of weather radar signals in the presence of ground clutter when only a small number of samples is available for processing. The ability of these methods to resolve weather signals from ground clutter in unfavorable conditions, i.e., when the clutter is stronger and its spectrum is narrower than that of the signal, is shown. Given a priori information about the ground clutter, Prony's method can be applied and simplified to derive a convenient formula for mean Doppler frequency estimation. This estimator may be considered as a generalization of the pulse-pair (PP) estimator and it can give satisfactory results in resolving weather signals from ground clutter for signal-to-noise ratios (SNRs) above 20 dB. The eigendecomposition-based minimum-norm (MN) method is applied to lower the SNR threshold to 10 dB. The performance of these estimators is compared with the signal-only lower bound and with that of a processor consisting of various ground clutter filters and the PP estimator.<
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