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Atmosphere 2017, 8(4), 72;

An Objective Prototype-Based Method for Dual-Polarization Radar Clutter Identification

LACS, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Australian Bureau of Meteorology, Melbourne, VIC 3008, Australia
Author to whom correspondence should be addressed.
Academic Editor: Richard Müller
Received: 22 January 2017 / Revised: 12 March 2017 / Accepted: 31 March 2017 / Published: 10 April 2017
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A prototype-based method is developed to discriminate different types of clutter (ground clutter, sea clutter, and insects) from weather echoes using polarimetric measurements and their textures. This method employs a clustering algorithm to generate data groups from the training dataset, each of which is modeled as a weighted Gaussian distribution called a “prototype.” Two classification algorithms are proposed based on the prototypes, namely maximum prototype likelihood classifier (MPLC) and Bayesian classifier (BC). In the MPLC, the probability of a data point with respect to each prototype is estimated to retrieve the final class label under the maximum likelihood criterion. The BC models the probability density function as a Gaussian mixture composed by the prototypes. The class label is obtained under the maximum a posterior criterion. The two algorithms are applied to S-band dual-polarization CP-2 weather radar data in Southeast Queensland, Australia. The classification results for the test dataset are compared with the NCAR fuzzy-logic particle identification algorithm. Generally good agreement is found for weather echo and ground clutter; however, the confusion matrix indicates that the techniques tend to differ from each other on the recognition of insects. View Full-Text
Keywords: prototype-based method; clutter identification; dual-polarization radar prototype-based method; clutter identification; dual-polarization radar

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Wen, G.; Protat, A.; Xiao, H. An Objective Prototype-Based Method for Dual-Polarization Radar Clutter Identification. Atmosphere 2017, 8, 72.

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