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An Efficient Information Fusion Method for Air Surveillance Systems

항공감시시스템을 위한 효율적인 정보융합 기법

  • Cho, Taehwan (Department of Electronics Engineering, Republic of Korea Air Force Academy) ;
  • Oh, Semyoung (Department of Electronics Engineering, Republic of Korea Air Force Academy) ;
  • Lee, Gil-Young (Department of Electronics Engineering, Republic of Korea Air Force Academy)
  • 조태환 (공군사관학교 전자통신공학과) ;
  • 오세명 (공군사관학교 전자통신공학과) ;
  • 이길영 (공군사관학교 전자통신공학과)
  • Received : 2016.05.26
  • Accepted : 2016.06.15
  • Published : 2016.06.30

Abstract

Among the various fields in the communications, navigation, and surveillance/air traffic management (CNS/ATM) scheme, the surveillance field, which includes an automatic dependent surveillance - broadcast (ADS-B) system and a multilateration (MLAT) system, is implemented using satellite and digital communications technology. These systems provide better performance than radar, but still incur position error. To reduce the error, we propose an efficient information fusion method called the reweighted convex combination method for ADS-B and MLAT systems. The reweighted convex combination method improves aircraft tracking performance compared to the original convex combination method by readjusting the weights given to these systems. In this paper, we prove that the reweighted convex combination method always provides better performance than the original convex combination method. Performance from the fusion of ADS-B and MLAT improves an average of 51.51% when compared to the original data.

자동종속 감시 시스템 (ADS-B; automatic dependent surveillance - broadcast) 시스템과 다변측정 항공감시 시스템(MLAT, multilateration) 시스템은 통신/항행/감시 및 교통관리 (CNS/ATM; communications, navigation, and surveillance/air traffic management)의 다양한 분야 중에서 감시분야에 속한다. ADS-B와 MLAT는 위성 및 디지털 통신 기술을 기반으로 구현되어 레이더 보다 성능이 뛰어나지만, 여전히 오차는 가지고 있다. 우는 이러한 오차를 줄이기 위해 reweighted convex combination method를 제안한다. Reweighted convex combination method는 기존의 convex combination method를 개선한 정보융합 기법으로 시스템에 주어지는 가중치를 재조정하여 항공기 추적 성능을 향상시킨다. reweighted convex combination method을 ADS-B와 MLAT에 적용 시켰을 때, 평균 51.51 %의 성능향상이 있었다.

Keywords

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