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Volumn 1, Issue , 2002, Pages 474-481

Particle filtering for multi-target tracking and sensor management

Author keywords

estimation; filtering; particle filter; Sensor management; Tracking

Indexed keywords

DISTRIBUTED COMPUTER SYSTEMS; ESTIMATION; FILTRATION; INFORMATION FUSION; MARKOV PROCESSES; MONTE CARLO METHODS; SENSORS; SURFACE DISCHARGES; TARGET TRACKING;

EID: 84901444236     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2002.1021192     Document Type: Conference Paper
Times cited : (128)

References (14)
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    • Following a moving target-monte carlo inference for dynamic bayesian models
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    • Gilks, W.R.1    Berzuini, C.2
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    • Sequential monte carlo for tracking a target in clutter noise
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    • Gordon, N.J.1    Doucet, A.2
  • 8
    • 0005716422 scopus 로고    scopus 로고
    • The scaled unscented transformation
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    • Julier, S. J., "The scaled unscented transformation," to appear in Automatica, 2002.
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    • Julier, S.J.1
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    • Monte carlo filter and smoother for non-gaussian nonlinear state space models
    • Kitagawa G., "Monte Carlo filter and smoother for non-Gaussian nonlinear state space models," J. Comput. Graph. Statist, vol. 5, pp. 1-25, 1996.
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    • Mahler R., "Engineering statistics for multi-object tracking," Proc. 2001 IEEE Workshop on Multi-Object Tracking, pp. 53-60, Vancouver, July 8, 2001.
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    • Random sets in information fusion: An overview
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    • Mahler R. "Random sets in information fusion: An overview," in J. Goutsias et. al. (eds.), Random Sets: Theory and Applications, pp. 129-164, Springer Verlag 1997.
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    • Random sets in dat fusion: Multi object state estimation as a foundation of data fusion theory
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.