Dual Polarization Ground Clutter Filtering

A novel dual polarization clutter filtering algorithm is developed to mitigate ground clutter effects on weather radar measurements. The statistical properties of the dual polarization radar data are utilized in time and spectral domains to reconstruct the weather signals/data from clutter contaminated signals. A multivariate Gaussian model is introduced to parametrize clutter and weather power spectrums, and the Maximum A Posterior (MAP) method is used for the parameter estimation. Instead of using the random phase, the phase of the retrieved weather spectrum is estimated based on the weather properties to be more accurate. The performance of the filtering algorithm is examined by applying it to the polarimetric data collected the KOUN radar, and the result is compared with that of several existing filtering algorithms. It is shown that the proposed algorithm can effectively mitigate clutter effects and substantially improve polarimetric weather radar data quality.