Unscented Particle Filter for Bearings-Only Tracking

Joseph J and LaViola J. A comparison of unscented and extended Kalrnan filtering for estimating quaternion motion. Proceedings of the American Control Conference Denver, 2003: 2435-2440.[2]Arulampalam S, Maskell S, and Gordon N, et al.. A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking[J].IEEE Trans. on Signal Processing.2002, 50(2):174-188[3]Gordon N, Salmond D, and Smith A F M. Novel approach to nonlinear non-Gaussian Bayesian state estimation. IEE Proc.-F. 1993, 140(2): 107-113.[4]Kong A, Liu J S, and Wong W H. Sequential imputations and Bayesian missing data problems[J].Journal of the American Statistical Association.1994, 89(4):278-288[5]Crisan D and Doucet A. A survey of convergence results on particle filtering methods for practitioners[J].IEEE Trans. on Signal Processing.2002, 50(3):736-746[6]Doucet A, Freitas N D, and Gordon N. Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001: 15-61.[7]Merwe R, Doucet A, and Freitas N D, et al.. The unscented particle filter. Technical Report Cued/F-Infeng/Tr 380, Cambridge University Department of Engineering, 2000: 1-45.[8]巴宏欣，赵宗贵，杨飞，等. 一种新的机动目标跟踪的多模型算法[J].电子与信息学报.2005,27(1):13-16浏览[9]Fearnhead P. Sequential Monte Carlo methods in filter theory. [Doctor degree paper], University of Oxford, 1998: 115-118.