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Indoor Position Detection Algorithm Based on Multiple Magnetic Field Map Matching and Importance Weighting Method

다중 자기센서를 이용한 실내 자기 지도 기반 보행자 위치 검출 정확도 향상 알고리즘

  • Kim, Yong Hun (Dept. of software convergence, Sejong University) ;
  • Kim, Eung Ju (Dept. of software convergence, Sejong University) ;
  • Choi, Min Jun (Dept. of software convergence, Sejong University) ;
  • Song, Jin Woo (School of Intelligent Mechatronic Engineering, Sejong University)
  • Received : 2018.11.13
  • Accepted : 2019.02.04
  • Published : 2019.03.01

Abstract

This research proposes a indoor magnetic map matching algorithm that improves the position accuracy by employing multiple magnetic sensors and probabilistic candidate weighting function. Since the magnetic field is easily distorted by the surrounding environment, the distorted magnetic field can be used for position mapping, and multiple sensor configuration is useful to improve mapping accuracy. Nevertheless, the position error is likely to increase because the external magnetic disturbances have repeated pattern in indoor environment and several points have similar magnetic field distortion characteristics. Those errors cause large position error, which reduces the accuracy of the position detection. In order to solve this problem, we propose a method to reduce the error using multiple sensors and likelihood boundaries that uses human walking characteristics. Also, to reduce the maximum position error, we propose an algorithm that weights according to their importance. We performed indoor walking tests to evaluate the performance of the algorithm and analyzed the position detection error rate and maximum distance error. From the results we can confirm that the accuracy of position detection is greatly improved.

Keywords

Acknowledgement

이 (성과물)은 산업통상자원부 '산업전문인력양성강화사업'의 재원으로 한국산업기술진흥원(KIAT)의 지원을 받아 수행된 연구임. (2018년 산업용 무인비행장치 전문인력 양성사업, 과제번호 N0002431)

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