• Title/Summary/Keyword: Heavy clutter density environment

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Hough Transform Clutter Reduction Algorithm for Piecewise Linear Path Active Sonar Target Detection and Tracking Improvement (구간선형기동 능동소나표적 탐지 추적 성능향상을 위한 허프변환 클러터제거 알고리즘)

  • Kim, Seong-Weon
    • The Journal of the Acoustical Society of Korea
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    • v.32 no.4
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    • pp.354-360
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    • 2013
  • In this paper, it is discussed that the detection and tracking performance of the piecewise linear path underwater target is improved using clutter reduction algorithm in heavy clutter density environment. Through clutter reduction algorithm using Hough Transform, measurements which represent clutter features are removed and the performance of target tracking on the remaining measurements is demonstrated applying CMKF-L(Converted Measurement Kalman Filter with Linearization) as tracking filter. Algorithm performance test is conducted using simulation data and real sea-trial data and by applying the proposed algorithm in heavy clutter density environment, it is confirmed that the target is tracked consistently and stably with clutter rejected measurements.

Single Ping Clutter Reduction Algorithm Using Statistical Features of Peak Signal to Improve Detection in Active Sonar System (능동소나 탐지 성능 향상을 위한 피크 신호의 통계적 특징 기반 단일 핑 클러터 제거 기법)

  • Seo, Iksu;Kim, Seongweon
    • The Journal of the Acoustical Society of Korea
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    • v.34 no.1
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    • pp.75-81
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    • 2015
  • In active sonar system, clutters degrade performance of target detection/tracking and overwhelm sonar operators in ASW (Antisubmarine Warfare). Conventional clutter reduction algorithms using consistency of local peaks are studied in multi-ping data and tracking filter research for active sonar was conducted. However these algorithms cannot classify target and clutters in single ping data. This paper suggests a single ping clutter reduction approach to reduce clutters in mid-frequency active sonar system using echo shape features. The algorithm performance test is conducted using real sea-trial data in heavy clutter density environment. It is confirmed that the number of clutters was reduced by about 80 % over the conventional algorithm while retaining the detection of target.