• Title, Summary, Keyword: chaff echo

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Chaff Echo Detecting and Removing Method using Naive Bayesian Network (나이브 베이지안 네트워크를 이용한 채프에코 탐지 및 제거 방법)

  • Lee, Hansoo;Yu, Jungwon;Park, Jichul;Kim, Sungshin
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.10
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    • pp.901-906
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    • 2013
  • Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

A Study on Chaff Echo Detection using AdaBoost Algorithm and Radar Data (AdaBoost 알고리즘과 레이더 데이터를 이용한 채프에코 식별에 관한 연구)

  • Lee, Hansoo;Kim, Jonggeun;Yu, Jungwon;Jeong, Yeongsang;Kim, Sungshin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.6
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    • pp.545-550
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    • 2013
  • In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

Characteristics of Chaff Echoes Observed by X-band Dual Polarization Radar (X-밴드 이중편파레이더에서 관측된 채프에코의 특성)

  • Seo, Eun-Kyoung;Park, Sora;Nam, Kyung-Yeub;Heo, Sol-Ip
    • Journal of the Korean earth science society
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    • v.34 no.1
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    • pp.1-12
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    • 2013
  • To effectively remove chaff echoes, which are often misidentified as precipitation echoes on weather radars, this study examines the relationship between the radar reflectivity and each of dual polarimetric parameters. The dual polarimetric parameters are collected only for the echo areas identified as chaff echoes on the NIMR X-band dual polarization radar. Overall, the polarimetric parameters (i.e., reflectivity, differential reflectivity, cross correlation coefficient, standard deviation of differential reflectivity and specific differential phase) for chaff echoes have a wider range of values than those for precipitation echoes and the chaff filaments tend to be horizontally oriented to radar beams. There appears to be a considerable overlap in the cross correlation coefficient range of chaff and precipitation echoes since some precipitation echoes have cross correlation coefficient lower than 0.8. Therefore, although the cross correlation coefficient is known to be a good variable in identifying and separating chaff echoes from precipitation echoes, it is suggested that additional care should be taken when using the cross correlation coefficient solely in removing chaff echoes.

Analysis of Quality Control Technique Characteristics on Single Polarization Radar Data (단일편파 레이더자료 품질관리기술 특성 분석)

  • Park, Sora;Kim, Heon-Ae;Cha, Joo Wan;Park, Jong-Seo;Han, Hye-Young
    • Atmosphere
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    • v.24 no.1
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    • pp.77-87
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    • 2014
  • The radar reflectivity is significantly affected by ground clutter, beam blockage, anomalous propagation (AP), birds, insects, chaff, etc. The quality of radar reflectivity is very important in quantitative precipitation estimation. Therefore, Weather Radar Center (WRC) of Korea Meteorological Administration (KMA) employed two quality control algorithms: 1) Open Radar Product Generator (ORPG) and 2) fuzzy quality control algorithm to improve quality of radar reflectivity. In this study, an occurrence of AP echoes and the performance of both quality control algorithms are investigated. Consequently, AP echoes frequently occur during the spring and fall seasons. Moreover, while the ORPG QC algorithm has the merit of removing non-precipitation echoes, such as AP echoes, it also removes weak rain echoes and snow echoes. In contrast, the fuzzy QC algorithm has the advantage of preserving snow echoes and weak rain echoes, but it eliminates the partial area of the contaminated echo, including the AP echoes.