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Volumn 3205, Issue , 2004, Pages 88-106

Particle filters for location estimation in ubiquitous computing: A case study

Author keywords

[No Author keywords available]

Indexed keywords

LOCATION; MONTE CARLO METHODS; PROBABILITY DISTRIBUTIONS;

EID: 33847770958     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30119-6_6     Document Type: Article
Times cited : (152)

References (27)
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    • UC San Diego, Computer Science and Engineering
    • Bhasker, E.S., Brown, S.W., Griswold, W.G.: Employing user feedback for fast, accurate, low-maintenance geolocationing. Technical Report CS2003-0765, UC San Diego, Computer Science and Engineering (2003)
    • (2003) Technical Report CS2003-0765
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    • 0035424017 scopus 로고    scopus 로고
    • Location systems for ubiquitous computing
    • This article is also excerpted in "IT Roadmap to a Geospatial Future," a 2003 report from the Computer Science and Telecommunications Board of the National Research Council.
    • Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34 (2001) 57-66 This article is also excerpted in "IT Roadmap to a Geospatial Future," a 2003 report from the Computer Science and Telecommunications Board of the National Research Council.
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    • Doucet, A., de Freitas, N., Gordon, N., eds.: Springer-Verlag, New York
    • Doucet, A., de Freitas, N., Gordon, N., eds.: Sequential Monte Carlo in Practice. Springer-Verlag, New York (2001)
    • (2001) Sequential Monte Carlo in Practice.
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    • 0034207091 scopus 로고    scopus 로고
    • A probabilistic approach to collaborative multi-robot localization
    • Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8 (2000) 325-244
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    • KLD-sampling: Adaptive particle filters
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.