Invariant polarimetric parameters for signal processing in weather radar

In this paper we introduce and check a new description of polarimetric parameters that are asymptotically invariant to the group of all monotonous transformations of input signals. In particular, this can give a possibility to estimate pure correlation between polarimetric signals independently on statistical characteristics of initial data. This copula-based approach is applied to signal processing in polarimetric meteorological radar.

[1]  Felix J. Yanovsky,et al.  Copula ambiguity function for wideband random radar signals , 2011, 2011 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS 2011).

[2]  Felix J. Yanovsky,et al.  Generalized copula ambiguity function application for radar signal processing , 2011, 2011 MICROWAVES, RADAR AND REMOTE SENSING SYMPOSIUM.

[3]  F. Yanovsky Simulation Study Of 10 Ghz Radar Backscattering From Clouds, And Solution Of The Inverse Problem of Atmospheric Turbulence Measurements , 1996 .

[4]  F.J. Yanovsky,et al.  Ultrawideband Signal Processing Algorithms for Radars and Sodars , 2006, 2006 3rd International Conference on Ultrawideband and Ultrashort Impulse Signals.

[5]  F. J. Yanovsky,et al.  Copula based dependence measure for polarimetric weather radar , 2015, 2015 16th International Radar Symposium (IRS).

[6]  R.B. Sinitsyn,et al.  Kernel estimates of the characteristic function for radar signal detection , 2005, European Radar Conference, 2005. EURAD 2005..

[7]  R. B. Sinitsyn,et al.  ACOUSTIC NOISE ATMOSPHERIC RADAR WITH NONPARAMETRIC COPULA BASED SIGNAL PROCESSING , 2012 .

[8]  Rustem B. Sinitsyn Copula based detection algorithm for MIMO ultrawideband noise radars , 2009, 2009 European Radar Conference (EuRAD).

[9]  V. Chandrasekar,et al.  Polarimetric Doppler Weather Radar , 2001 .

[10]  D. Zrnic,et al.  Doppler Radar and Weather Observations , 1984 .

[11]  5 B . 3 RADAR DOPPLER POLARIMETRY APPLIED TO PRECIPITATION MEASUREMENTS : INTRODUCTION OF THE SPECTRAL DIFFERENTIAL REFLECTIVITY , 2001 .

[12]  Sebastián M. Torres,et al.  The Autocorrelation Spectral Density for Doppler-Weather-Radar Signal Analysis , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Felix J. Yanovsky,et al.  Inferring microstructure and turbulence properties in rain through observations and simulations of signal spectra measured with Doppler–polarimetric radars , 2011 .