Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Outlier Reduction using C-SCGP for Target Localization based on RSS/AOA in Wireless Sensor Networks

무선 센서 네트워크에서 C-SCGP를 이용한 RSS/AOA 이상치 제거 기반 표적 위치추정 기법

  • Kang, SeYoung (Department of Computer Science and Engineering, Korea University) ;
  • Lee, Jaehoon (Department of Computer Science and Engineering, Korea University) ;
  • Song, JongIn (School of Electronic Engineering and Computer Science, Gwangju Institute of Science and Technology) ;
  • Chung, Wonzoo (Department of Artificial Intelligence, Korea University)
  • 강세영 (고려대학교 컴퓨터학과) ;
  • 이재훈 (고려대학교 컴퓨터학과) ;
  • 송종인 (광주과학기술원 전기전자컴퓨터공학부) ;
  • 정원주 (고려대학교 인공지능학과)
  • Received : 2021.09.01
  • Accepted : 2021.11.20
  • Published : 2021.11.28
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Abstract

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

본 논문에서는 무선 센서 네트워크에서 이상치를 포함한 수신 신호 강도와 신호의 도달 각도 측정치 기반의 표적위치추정 성능 저하를 방지하기 위한 이상치 검출 알고리즘 C-SCGP를 제안한다. 센서 오작동, 재밍, 심한 잡음과 같은 다양한 이상치 원인으로 인해 표적 위치추정 정확도가 크게 떨어질 수 있어, 모든 이상치를 탐지하고 제거하는 것이 중요하다. 이러한 이상치를 제거하기 위해 single cluster graph partitioning (SCGP) 알고리즘이 널리 사용되고 있다. 기존의 SCGP 알고리즘은 hyperparameter 최적화를 통한 threshold 설정과 이상치 확률 계산이 필수적이므로 다양한 분야에 효율적인 적용이 제한되어왔다. 본 논문에서 제안된 continuous-SCGP (C-SCGP) 알고리즘은 이러한 SCGP의 약점을 극복한다. 다양한 잡음 환경에서 threshold 설정과 이상치 확률 계산이 필요 없는 제안된 C-SCGP 알고리즘과 threshold 설정과 이상치 확률 계산을 요구하는 SCGP 알고리즘의 이상치 제거 성능이 같음을 최종 추정된 표적의 RMSE 성능을 통하여 검증하였다.

Keywords

Acknowledgement

This study is supported from Electronic Warfare Research Center (EWRC) at Gwangju Institute of Science and Technology (GIST), originally funded by Defense Acquisition Program Administration(DAPA) and Agency for Defense Development (ADD).

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