Earticle

초록

영어

The purpose of this paper is introducing WiFi based EKF(Extended Kalman Filter) method for indoor positioning. The advantages of our EKF method include：1) Any special equipment dedicated for positioning is not required. 2) Implementation of EKF does not require off-line phase of fingerprinting methods. 3) The EKF effectively minimizes squared deviation of the trilateration method. In order to experimentally prove the advantages of our method, we implemented indoor positioning systems making use of the K-NN(K Nearest Neighbors), Bayesian, decision tree, trilateration, and our EKF methods. Our experimental results show that the average-errors of K-NN, Bayesian and decision tree methods are all close to 2.4 meters whereas the average errors of trilateration and EKF are 4.07 meters and 3.528 meters, respectively. That is, the accuracy of our EKF is a bit inferior to those of fingerprinting methods. Even so, our EKF is accurate enough to be used for practical indoor LBS systems. Moreover, our EKF is easier to implement than fingerprinting methods because it does not require off-line phase.

목차

Abstract
 1. 서론
 2. 기존의 연구
  2.1 K-NN 방법
  2.2 베이시안 방법
  2.3 의사결정나무 방법
  2.4 삼각측량기법
  2.5 확장칼만필터
 3. 제안방법 -Wi-Fi 흔병에서 확장칼만필터 알고리즘
 4. 실험
 5. 결론
 참고문헌

키워드

저자

• 임재걸 [ Jaegeol Yim | 동국대학교 컴퓨터멀티미디어학과 교수, 연구원 ]
• 박찬식 [ Chansik Park | 충북대학교 전기전자컴퓨터공학부 교수 ]
• 주재훈 [ Jaehun Joo | 동국대학교 경상학부 교수 ] 교신저자
• 정승환 [ Seunghwan Jeong | 동국대학교 컴퓨터멀티미디어학과 교수, 연구원 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005