A good portion of modern signal processing deals with statistical signal processing and array signal processing, in which models of both the signal and the noise are used to attain precise estimates of the underlying signal parameters. These parameters include frequencies of sinusoids, angles of arrival of wavefronts, scatterer location of radar targets, etc. The explicit use of a signal-noise model and the use of an optimal statistical estimator leads to an accuracy and resolution which is clearly superior to the fast Fourier transform (FFT). At low signal-to- noise ratios, the estimators can be made as robust as the FFT. We present applications of superresolution signal estimators to synthetic aperture radar (SAR) and inverse SAR imaging.
Theagenis J. Abatzoglou,
"Superresolution signal processing and its applications", Proc. SPIE 2562, Radar/Ladar Processing and Applications, (18 August 1995); doi: 10.1117/12.216968; https://doi.org/10.1117/12.216968