Improvement of UAV Attitude Information Estimation Performance Using Image Processing and Kalman Filter
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
In recent years, researches utilizing UAV for military purposes such as precision tracking and batting have been actively conducted. In order to track the preceding flight, there has been a previous research on estimating the attitude information of the flight such as roll, pitch, and yaw using images taken from the rear UAV. In this study, we propose a method to estimate the attitude information more precisely by applying the Kalman filter to the existing image processing technique. By applying the Kalman filter to the estimated attitude data using image processing, we could reduce the estimation error of the attitude angle significantly. Through the simulation experiments, it was confirmed that the estimation using the Kalman filter can estimate the posture information of the aircraft more accurately.
Abstract
In recent years, researches utilizing UAV for military purposes such as precision tracking and batting have been actively conducted. In order to track the preceding flight, there has been a previous research on estimating the attitude information of the flight such as roll, pitch, and yaw using images taken from the rear UAV. In this study, we propose a method to estimate the attitude information more precisely by applying the Kalman filter to the existing image processing technique. By applying the Kalman filter to the estimated attitude data using image processing, we could reduce the estimation error of the attitude angle significantly. Through the simulation experiments, it was confirmed that the estimation using the Kalman filter can estimate the posture information of the aircraft more accurately.
참고문헌 (19)
- Emmanouil N. Barmpounakis, "Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges", International Journal of Transportation Science and Technology, 5(3), pp.111~122 (2016), Elsevier BV[DOI]
- 이정필, "Cam-Shift 알고리즘을 이용한 경비드론 융합서비스 기법", 한국융합학회논문지, 7(5), pp.29~34 (2016), 한국융합학회[DOI]
- 문형진, "사물인터넷 통신기술에 내재된 보안위협과 대응 전략", 융합정보논문지, 6(2), pp.37~44 (2016), 중소기업융합학회[DOI]
- 박광옥, "홈 IoT에서 SSDP 반사체 공격에 대한 대응기법", 융합정보논문지, 7(2), pp.1~9 (2017), 중소기업융합학회[DOI]
- 김기봉, "CCTV 영상 정보와 재난재해 인식 및 실시간 위기 대응 시스템의 융합에 관한 연구", 한국융합학회논문지, 8(3), pp.15~22 (2017), 한국융합학회[DOI]
- Y. B. Sebbane., "Smart Autonomous Aircraft:Flight Control and Planning for UAV" (2016), CRC Press
- Y. Wang, "Vehicle State Estimation Using GPS", IMU Integration, 2011 (2011), 28~31[DOI]
- 폴 퀴로즈, "A Study on Attitude Estimation of UAV Using Image Processing", 융합정보논문지, 7(5), pp.137~148 (2017), 중소기업융합학회[DOI]
- H.D. Cheng, "Color image segmentation: advances and prospects", Pattern Recognition, 34(12), pp.2259~2281 (2001), Elsevier BV[DOI]
- 황우성, "영역 분할과 로컬 히스토그램을 이용한 저조도 환경의 영상 향상 방법과 차량 블랙박스 융합", 한국융합학회논문지, 7(6), pp.1~6 (2016), 한국융합학회[DOI]
- Jae-Chern Yoo, "Template matching of occluded object under low PSNR", Digital Signal Processing, 23(3), pp.870~878 (2013), Elsevier BV[DOI]
- L. Ding, "On the Canny Edge Detector", Pattern Recognition, 34(3), pp.721~725 (2001)
- Jing Cui, "Corners detection on finger vein images using the improved Harris algorithm", Optik, 125(17), pp.4668~4671 (2014), Elsevier BV[DOI]
- Priyanka Mukhopadhyay, "A survey of Hough Transform", Pattern Recognition, 48(3), pp.993~1010 (2015), Elsevier BV[DOI]
- S. M. Ross., "Introductory Statistics: Chapter 12 -Linear Regression", pp.519~584 (2017), Academic Press
- 김영임, "청소년의 구강건강증진행위에 미치는 영향요인 연구", 보건의료산업학회지, 11(2), pp.129~142 (2017), 보건의료산업학회
- G. Welch, "An Introduction to the Kalman Filter" (2001), SIGGRAPH. ACM, Inc.
- "How X_Plane Works" (2017)[URL]
- "Root-mean-square deviation" (2018)[URL]