• Title/Summary/Keyword: 3D Radar Object Tracking

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3D Radar Objects Tracking and Reflectivity Profiling

  • Kim, Yong Hyun;Lee, Hansoo;Kim, Sungshin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.4
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    • pp.263-269
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    • 2012
  • The ability to characterize feature objects from radar readings is often limited by simply looking at their still frame reflectivity, differential reflectivity and differential phase data. In many cases, time-series study of these objects' reflectivity profile is required to properly characterize features objects of interest. This paper introduces a novel technique to automatically track multiple 3D radar structures in C,S-band in real-time using Doppler radar and profile their characteristic reflectivity distribution in time series. The extraction of reflectivity profile from different radar cluster structures is done in three stages: 1. static frame (zone-linkage) clustering, 2. dynamic frame (evolution-linkage) clustering and 3. characterization of clusters through time series profile of reflectivity distribution. The two clustering schemes proposed here are applied on composite multi-layers CAPPI (Constant Altitude Plan Position Indicator) radar data which covers altitude range of 0.25 to 10 km and an area spanning over hundreds of thousands $km^2$. Discrete numerical simulations show the validity of the proposed technique and that fast and accurate profiling of time series reflectivity distribution for deformable 3D radar structures is achievable.

A Study on White Space Search of Wireless Signal based Passive Tracking Technology using Enhanced Search Formula of Patent Analysis (개선된 검색식 기반 특허분석을 통한 무선신호 기반 Passive Tracking 공백기술 도출에 관한 연구)

  • Lee, Hangwon;Kim, Youngok
    • Journal of the Society of Disaster Information
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    • v.17 no.4
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    • pp.802-816
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    • 2021
  • Purpose: In this paper, we propose a direction of future research and development to be carried out in the passive tracking field by deriving a white space with enhanced search formula of patent analysis. Method: In this paper, we derive a white space by identifying the direction and the flow of technology change and by matrixing the object and solution through extensive patent search with enhanced search formula and analysis in the field of passive tracking technology. Result: By the proposed scheme, 'multi-target positioning and tracking' and '3D positioning technology' using artificial intelligence, adaptive/hybrid positioning technology, and radar/antenna were derived as white space technologies and confirmed with absence of any services or products. Conclusion: The derived white space technologies from this paper are the areas where patent applications are not active and there are not many prior patents, thus it is necessary to secure the rights through more active R&D and patent application activities.

Development of GK2A Convective Initiation Algorithm for Localized Torrential Rainfall Monitoring (국지성 집중호우 감시를 위한 천리안위성 2A호 대류운 전조 탐지 알고리즘 개발)

  • Park, Hye-In;Chung, Sung-Rae;Park, Ki-Hong;Moon, Jae-In
    • Atmosphere
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    • v.31 no.5
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    • pp.489-510
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    • 2021
  • In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.