S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems, 1999.

R. Mahler, A theoretical foundation for the stein-winter probability hypothesis density (phd) multi-target tracking approach, Proc. MSS Nat'l Symp. on Sensor and Data Fusion, 2000.

W. Ng, J. Li, S. Godsill, and J. Vermaak, A hybrid approach for online joint detection and tracking for multiple targets, 2005 IEEE Aerospace Conference
DOI : 10.1109/AERO.2005.1559504

N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, Radar and Signal Processing, IEE Proceedings, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

C. P. Robert and G. Casella, Monte-Carlo Statistical Methods, 2004.

W. R. Gilks and C. Berzuini, Following a moving target-Monte Carlo inference for dynamic Bayesian models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.1, pp.127-146, 2001.
DOI : 10.1111/1467-9868.00280

F. Septier, S. K. Pang, A. Carmi, and S. Godsill, On MCMC-Based particle methods for Bayesian filtering: Application to multitarget tracking, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009.
DOI : 10.1109/CAMSAP.2009.5413256

URL : https://hal.archives-ouvertes.fr/hal-00566647

D. Schuhmacher, B. Vo, and B. Vo, A Consistent Metric for Performance Evaluation of Multi-Object Filters, IEEE Transactions on Signal Processing, vol.56, issue.8, pp.3447-3457, 2008.
DOI : 10.1109/TSP.2008.920469

J. Vermaak, S. Maskell, M. Briers, and P. Perez, A unifying framework for multi-target tracking and existence, 2005 7th International Conference on Information Fusion, pp.25-28, 2005.
DOI : 10.1109/ICIF.2005.1591862

K. Gilholm, S. J. Godsill, S. Maskell, and D. Salmond, Poisson models for extended target and group tracking, Signal and Data Processing of Small Targets 2005
DOI : 10.1117/12.618730

O. Cappé, S. J. Godsill, and E. Moulines, An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo, Proceedings of the IEEE, vol.95, issue.5, pp.899-924, 2007.
DOI : 10.1109/JPROC.2007.893250

C. Berzuini, N. G. Best, W. R. Gilks, and C. Larizza, Dynamic Conditional Independence Models and Markov Chain Monte Carlo Methods, Journal of the American Statistical Association, vol.24, issue.440, pp.1403-1412, 1997.
DOI : 10.1080/01621459.1997.10473661

Z. Khan, T. Blach, and F. Dellaert, MCMC-based particle filtering for tracking a variable number of interacting targets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.11, pp.1805-1819, 2005.
DOI : 10.1109/TPAMI.2005.223

A. Golightly and D. J. Wilkinson, Bayesian sequential inference for nonlinear multivariate diffusions, Statistics and Computing, vol.55, issue.398, pp.323-338, 2006.
DOI : 10.1007/s11222-006-9392-x

F. Septier, A. Carmi, S. K. Pang, and S. J. , Multiple Object Tracking Using Evolutionary and Hybrid MCMC- Based Particle Algorithms, 15th IFAC Symposium on System Identification, 2009.