A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection.

We present a cylindrical dual-polarization phased array antenna for weather surveillance radars that features high isolation, matched copolar beams, low sidelobe levels, and adaptive null steering. We present a crossed dipole antenna element to replace the patch antenna on the currently deployed cylindrical polarimetric phased array radar (CPPAR). The crossed dipole element offers suppressed surface wave and lower coupling between adjacent elements of an array. As a result, we achieved lower backlobe and sidelobe levels, and a higher match between copolar beams of CPPAR compared to the currently deployed multi-layer patch antenna. Further reduction of the sidelobe level and adaptive null steering are obtained using a modified particle swarm optimization. The proposed null steering mitigates the interference among the four concurrent beams of a cylindrical phased array radar. The improvement in CPPAR radiation characteristics has been verified by comparing the presented crossed dipole antenna's radiation patterns and the existing aperture coupled patch antenna. The proposed crossed dipole phased array can benefit national weather radar networks to provide accurate multiparameter measurements enabling reliable observation of severe weather phenomena.

This letter presents initial weather measurements with a cylindrical polarimetric phased array radar (CPPAR) demonstrator developed at The University of Oklahoma. The overall system specifications, waveform design, beam pattern measurement, and beam-to-beam calibration of the CPPAR demonstrator are presented. The weather observations of convective precipitation are provided, employing a single-beam mechanical scan and commutating beam electronic scan. Measurement results from these two scan modes are compared, and the error statistics are derived and discussed. A new feature of the CPPAR commutating beam electronic scan in clutter detection is observed and explained.

A novel 3D discriminant function is introduced as part of a ground clutter detection algorithm for improving weather radar observations. The 3D discriminant function utilizes the phase fluctuations of the received signals for horizontal and vertical polarizations and the dual-scan cross-correlation coefficient. An optimal decision based on the 3D discriminant function is made using a simple Bayesian classifier to distinguish clutter from weather signals. For convenience of use, a multivariate Gaussian mixture model is used to represent the probability density functions of discriminant functions. The model parameters are estimated based on the maximum likelihood using the expectation–maximization (ML-EM) method. The performance improvements are demonstrated by applying the proposed detection algorithm to radar data collected by the polarimetric Norman, Oklahoma (KOUN), weather radar. This algorithm is compared to other clutter detection algorithms and the results indicate that, using the proposed detection algorithm, a better probability of detection can be achieved.

Beamforming in Wireless Networks | InTechOpen, Published on: 2016-12-14. Authors: Mohammad-Hossein Golbon-Haghighi