Radar signal quality improvements by spectral processing of dual-polarization radar measurements

Next to calibration, clutter mitigation is the second oldest problem in radar remote sensing. Introduction of Doppler spectral processing has dramatically increased quality of radar measurements, by filtering out ground clutter. Nonetheless, not all clutter types are easy to suppress. Doppler properties of sea clutter, birds and insects, chaff and RF interferences are significantly different from ground clutter and challenge standard Doppler clutter filters. To mitigate those types of clutter a new approach is needed.
In this study, spectral analysis of dual-polarization radar measurements is applied to improve quality of radar observations (Moisseev et al., 2000, 2002; Unal and Moisseev, 2004; Moisseev and Chandrasekar, 2009; Bachmann and Zrnić, 2007). The proposed approach gives a greater flexibility for discrimination between different types of scattering sources present in a radar observation volume. The proposed spectral filter design uses a fuzzy logic classification algorithm applied to textures of spectral differential phase and reflectivity and spectral decomposition of co-polar correlation coefficient (Moisseev and Chandrasekar, 2009). Then by rejecting spectral lines that are affected by non-weather signals the clutter filter is defined.