Nonlinear multiple model particle filters algorithm for tracking multiple targets

Journal title

Archives of Control Sciences




No 1

Publication authors

Divisions of PAS

Nauki Techniczne


Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.

Aims and Scope: Archives of Control Sciences publishes papers in the broadly understood field of control science and related areas while promoting the closer integration of the Polish, as well as other Central and East European scientific communities with the international world of science.


Committee of Automatic Control and Robotics PAS




ISSN 1230-2384


Sebbagh A. (2009), Particle filtering for air craft tracking with bearings-only measurement, null. ; Hue C. (2002), Tracking multiple objects with particle filtering, IEEE Trans. on Aerospace and Electronic Systems, 38, 3, 791, ; Hue C. (2002), Sequential Monte Carlo methods for multiple target tracking and data fusion, IEEE Trans. on Signal Processing, 50, 2, 309, ; Arnaud D. (1998), On sequential simulation-based methods for Bayesian filtering. ; Arnaud D. (2000), Convergence of sequential Monte Carlo methods. ; Arnaud D. (2001), Sequential Monte Carlo Methods in Practice, 525. ; Gordon N. (1997), A hybrid bootstrap filter for target tracking in clutter, IEEE Trans. on Aerospace and Electronic Systems, 33, 1, 353, ; Gordon N. (1993), Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings, Pt. F, Radar and Signal Processing, 140, 2, 107, ; Kushner H. (1967), Approximations to optimal nonlinear filtering, IEEE Trans. on Automatic Control, AC-12, 5, 546, ; Kushner H. (1967), Dynamical equations for optimum nonlinear filtering, J. of Differential Equations, 3, 179, ; Wang H. (1999), Precision large scale air traffic surveillance using an IMM estimator with assignment, IEEE Trans. on Aerospace and Electronic Systems, AES-35, 1, 255, ; Isard M. (1998), CONDENSATION-Conditional density propagation for visual tracking, Int. J. of Computer Vision, 29, 1, 5, ; Uhhmann J. (1992), Algorithms for multiple target tracking, American Scientist, 80, 2, 128. ; Kong A. (1994), Sequential imputation method and Bayesian missing data problems, J. of American Statistical Association, 89, 278, ; Liu J. (1996), Metropolized independent sampling with comparison to rejection sampling and importance sampling, Statistics and Computing, 6, 113, ; J. Maccormick: Probabilistic modelling and stochastic algorithms for visual localisation and tracking. Ph.D. dissertation, University of Oxford, Jan., 2000. ; Djouadi M. (2005), IMM based UKF and IMM base EKF algorithms for tracking highly manoeuverable target, Archives of Control Sciences, 15, 1, 19. ; Djouadi M. (2005), A nonlinear algorithm for maneuvering target visual-based tracking, null, 61. ; R. Van Der Merwe (2000), The unscented particle filter. ; Salmond D. (1990), Mixture reduction algorithms for target tracking in clutter, SPIE Signal and Data Processing of Small Targets, 1305, 434. ; Bar-Shalom Y. (2001), Estimation with applications to tracking and navigation,