지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.10
- 수록면
- 868 - 872 (5page)
이용수
초록· 키워드
오류제보하기
Particle filter is a Monte Carlo simulation method that designed to approximate nonlinear problem and tracks the state of the dynamic system. Over the past decade, particle filters have been successfully applied in wide variety of applications. By using geographical information system (GIS) database, particle filter applications involve tracking of underwater (UW) vessels, surface ships, cars and aircraft. Particle filter also contributed in robotics community, where the algorithm can solve the simultaneous localization and mapping (SLAM) problem. Since multiple targets can be tracked from a video stream using particle filter, visual tracking is another interesting application of particle filter. However, particle filter is rarely used in the flood water level prediction and tracking applications. This paper proposes flood water level prediction and tracking using Sampling Importance Resampling (SIR) particle filter which is one of particle filter variations. As the effectiveness of the particle filter depends on the parameters stated in the algorithm, the parameters are varied to analyze the performance result. The parameters are number of particles and number of time step. From the simulation, the performance result of particle filter is quite impressive to look into.
목차
Abstract
I. INTRODUCTION
II. THEORY OF PARTICLE FILTER
IV. RESULTS AND DISCUSSION
VI. CONCLUSIONS
REFERENCES
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