TWR 최소 값 선택기법과 최소자승법을 적용한 이동객체 위치인식 알고리즘에 대한 연구 :A Study on Moving Object Localization Algorithm using TMVS and Least-Squares Method

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자료유형: 학위논문

저자정보: 정무경 (동명대학교, 동명대학교 대학원)

지도교수: 이동명

발행연도: 2013

저작권: 동명대학교 논문은 저작권에 의해 보호받습니다.

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이 논문의 연구 히스토리 (5)

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2013

TWR 최소 값 선택기법과 최소자승법을 적용한 이동객체 위치인식 알고리즘에 대한 연구

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NLOS환경에서의 최소자승법을 적용한 위치인식 보정 알고리즘

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1988

통계학-최우추정법과 최소자승법의 비교

정무경 고시계 1988.10 전문잡지

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Chirp Spread Spectrum(CSS)기반 Time of Flight(TOF)방식의 Symmetric Double Sided - Two Way Ranging(SDS-TWR)은 최근 Wireless Personal Area Network(WPAN) 분야에서 널리 사용되는 레인징 방식이다. 그러나 SDS-TWR은 전파 및 장애물에 의한 간섭으로 레인징 오차가 발생하기 떄문에 정교한 위치인식 정확도를 얻기 위해서는 거리측정 기법과 위치인식을 통해 얻어지는 좌표 값을 보정하는 알고리즘이 필요하다.
본 논문에서는 레인징 기법인 SDS-TWR의 문제점을 분석하고 이를 해결하기 위하여 TWR 최소 값 선택 기법(TMVS: TWR Minimum Value Select, 이하 TMVS로 표기함)을 제안하였다. 그리고 최소자승법을 적용한 위치인식 시스템을 설계하고 실험을 통하여 성능 분석을 실시하였다.
제안하는 이동객체 위치인식 알고리즘의 성능 분석을 위해 고정, 직선이동, 방향전환 1회, 방향전환 2회의 총 4 개의 시나리오를 만들어 실험하였다. 실험결과 시나리오 1(고정)의 환경에서 제안하는 보정 알고리즘을 이용한 위치인식의 성능은 삼변측량법을 이용한 위치인식의 성능보다 평균오차가 43.46% 감소하였다. 또한, 시나리오 2(직선이동)의 환경에서도 제안하는 보정 알고리즘을 이용한 위치인식의 성능은 52.90%의 평균 오차가 감소하였다. 그리고 시나리오 3(방향전환 1회), 4(방향전환 2
회)에서도 제안하는 보정 알고리즘을 이용한 위치인식의 성능도 삼변측량법을 이용한 위치인식의 성능 보다 평균 오차가 각각 58.61%, 67.56%감소하는 것을 확인하였다. 결과적으로TWR 최소 값 선택 기법과 최소자승법을 적용한 위치인식 알고리즘이 기존의 위치인식 방법보다 신뢰성있는 보정 알고리즘으로 판단된다. 따라서 다양한 분야에서 위치인식 시스템의 구현 시 본 알고리즘을 적용하면 위치인식의 성능개선에 큰 도움을 줄 수 있을 것으로 기대된다.

TOF(Time of Flight) method based on CSS(Chirp Spread Spectrum) is typical implementation technology for a localization system. But SDS-TWR (Symmetrical Double-Sided Two Way Ranging) of CSS becomes an occurrence of ranging error because of interference according to an environment with radio wave and an obstacle. Thus a
compensation technique is needed to compensation it for a
development of localization system. So a compensation algorithm ofobtained coordinate values through localization must be studied. In this research is proposed TWR Minimum Value Selection(TMVS) technique to decide what is analyzed from problems of SDS-TWR ranging method. Therefore a localization system is designed on the compensation algorithm applied to Least Square Method(LMS) for accuracy performance optimization of localization of trilateration. It was accomplished performance analysis by experiment results.
It was designed tested on total four scenario`s types; fixed, straight moving, direction conversion a time and direction conversion two times for performance analysis. In test results scenario 1(fixed) environment, the localization applying the proposed compensation algorithm was
decreased average errors by 43.46% than the localization applying trilateration method. Also, in scenario 2(straight moving) environment the localization applying the proposed compensation algorithm was decreased average errors by 52.90%. In case of scenario 3(directionconversion a time) and scenario 4(direction conversion two times), the
localization applying proposed compensation algorithm was decreased average errors by each 58.61% and 67.56% more than the localization applying trilateration method.
In conclusion, the localization techniques applying TMVS technique and LMS method are determined reliable compensation algorithms for a localization more than an existing localization method. Thus we expect to give more performance improvement applying the proposed compensation algorithm when considering an implementation of localization system.

#SDS-TWR #최소자승법 #삼변측량법 #위치인식 #이동객체

Ⅰ. 서론·················································································································1
Ⅱ. 관련연구········································································································3
1. CSS 기반 레인징 기술 ···········································································3
가. TWR ·····································································································3
나. SDS-TWR ··························································································4
2. 위치인식 기술···························································································6
가. 삼변측량법 ···························································································6
나. 칼만필터를 이용한 보정 알고리즘 ·················································7
다. 오일러 공식을 이용한 보정 알고리즘 ···········································9
Ⅲ. 제안하는 거리측정 알고리즘·······························································11
1. SDS-TWR의 레인징 문제점 분석····················································11
2. TMVS 제안 ····························································································12
가. TMVS ································································································12
나. TMVS 흐름도··················································································13
3. 실험 및 성능분석 ···················································································14
가. 실험환경 ·····························································································14
나. 성능분석 ·····························································································14
Ⅳ. 제안하는 위치인식 보정 알고리즘····················································16
1. 설계 고려사항·························································································16
2. 최소자승법 ·······························································································17
3. 제안하는 위치인식 보정 알고리즘 ·····················································19
가. TMVS (1단계) ·················································································20
나. 삼변측량법(2단계) ···········································································21
다. 최소자승법(3단계) ···········································································22
4. 시나리오 구성·························································································24
Ⅴ. 실험 및 성능분석·····················································································25
1. 실험 환경 ·································································································25
2. 시나리오별 실험 결과 ···········································································29
가. 시나리오 1(고정) ·············································································29
나. 시나리오 2(직선이동) ·····································································32
다. 시나리오 3(방향전환 1회) ·····························································35
라. 시나리오 4(방향전환 2회) ··················

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