Sequential monte carlo implementation of the phd filter for multi-target tracking
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[1] G. Matheron. Random Sets and Integral Geometry , 1976 .
[2] Adrian Baddeley,et al. ICM for Object Recognition , 1992 .
[3] Yakov Bar-Shalom,et al. Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .
[4] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .
[5] T. Mattfeldt. Stochastic Geometry and Its Applications , 1996 .
[6] I. R. Goodman,et al. Mathematics of Data Fusion , 1997 .
[7] Daryl J. Daley,et al. An Introduction to the Theory of Point Processes , 2013 .
[8] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[9] R. Mahler. A Theoretical Foundation for the Stein-Winter "Probability Hypothesis Density (PHD)" Multitarget Tracking Approach , 2000 .
[10] Olaf Wolkenhauer,et al. Random-sets: theory and applications , 2001 .
[11] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[12] Charles J. Geyer,et al. Likelihood inference for spatial point processes , 2019, Stochastic Geometry.