Detection of Ground Clutter for Dual-Polarization Weather Radar Using a Novel 3D Discriminant Function

Mohammad-Hossein Golbon-Haghighi School of Electrical and Computer Engineering, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Guifu Zhang School of Meteorology, and School of Electrical and Computer Engineering, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mohammad-Hossein Golbon-Haghighi, golbon@ou.edu; Guifu Zhang, guzhang1@ou.edu

Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mohammad-Hossein Golbon-Haghighi, golbon@ou.edu; Guifu Zhang, guzhang1@ou.edu
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