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

Abstract

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.

Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis

“…The improved WGAN (WGAN-GP) allows the discriminator to learn more complex functions and reduces the vanishing and exploding gradient problem. Equations (12) and (13) represent the loss functions of the discriminator and generator, respectively.…”

“…Sea clutter [5], ground clutter [6], and anomalous propagation [7] are typical examples of the non-meteorological echoes and make a quality control process [8] complicated by their similar characteristics to meteorological echoes. Therefore, a large number of domestic and foreign scholars have paid attention to implementing precise prediction methods or automated quality control processes to alleviate the experts' tasks based on statistical [9] and machine learning methods, such as fuzzy logic [10], Bayesian [11], random forest [12], and clustering [13]. Furthermore, lots of meteorological researchers and forecasters have recently focused on deep learning-based approaches, which have shown their superior capability to solve real-world problems in various fields [14].…”

A Gaussian mixture method for specific differential phase retrieval at X-band frequency

“…One of interpretations of the Gaussian mixture is to view each distribution as a cluster with a Gaussian probability density, while the individual data point is attributed to a specific cluster or a weight toward the cluster, regarded as unsupervised learning (Hastie et al, 2009). The clustering procedures based on the Gaussian mixture model have been applied to the identification of storm structure (Veneziano and Villani, 1996), as well as the particle identification at S-band (Wen et al, 2015(Wen et al, , 2016b(Wen et al, , 2017 and X-band (Wen et al, 2016a) frequencies. Furthermore, the Gaussian mixture model can be extended to fit a set of unknown parameters in the prior probability of the Bayesian framework, forming a Bayesian-Gaussian mixture model (Li et al, 2012).…”

“…It is well known that the effect of clutter can be reduced by applying a spectrum filter to the time-series data (e.g., May and Strauch, 1998;Hubbert et al, 2009). However, some residual clutter echoes are still shown on the radar measurements including dp (Wen et al, 2017). Therefore, the clutter needs to be well handled in GMM, prior to the deviation of the regression model based on the joint PDF.…”

“…Its range derivative, also called the specific differential phase (K dp ), has some advantages over Z H and Z DR (Zrnić and , including insensitivity to attenuation, clutter, partial beam blockage, and radar absolute calibration. The specific differential phase has played a key role in various meteorological applicationssuch as hydrometeor classification (Lim et al, 2005;Park et al, 2009), raindrop size distribution retrieval (Bringi et al, 2002;Williams et al, 2014), and quantitative precipitation estimation (Ryzhkov et al, 2005;Cifelli et al, 2011) -since K dp is a phase variable independent of Z H and Z DR and almost linearly proportional to rain rate. A linear regression model has been developed to derive K dp from the slope of the range profile of dp measured by the polarimetric radars.…”