Particle filtering for multi-target tracking and sensor management
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A. Doucet | M. Davy | C. Andrieu | B. Vo
[1] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .
[2] Ronald P. S. Mahler,et al. Random Sets in Information Fusion an Overview , 1997 .
[3] Shozo Mori,et al. Random Sets in Data Fusion Multi-Object State-Estimation as a Foundation of Data Fusion Theory , 1997 .
[4] Shozo Mon,et al. Random sets in data fusion. Multi-object state-estimation as a foundation of data fusion theory , 1997 .
[5] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[6] Arnaud Doucet,et al. Sequential Monte Carlo for maneuvering target tracking in clutter , 1999, Optics & Photonics.
[7] A. Doucet,et al. Sequential MCMC for Bayesian model selection , 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.
[8] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[9] W. Gilks,et al. Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .
[10] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[11] R. Mahler. Engineering statistics for multi-object tracking , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.
[12] Simon J. Julier,et al. The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).
[13] Christophe Andrieu,et al. Efficient particle filtering for Jump Markov Systems , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.