A Dual Particle Filter for State and Parameter Estimation in Nonlinear System

[1] Kalman R. A new approach to linear filtering and predictionproblem. Trans. of the ASME-Journal of Basic Engineering,1960, 82(D): 34-45. [2] Jazwinski A H. Stochastic Processes and Filtering Theory.New York: Academic press, 1970: 281-286. [3] Dempster A P, Laird N M, and Rubin D B. Maximumlikelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society, 1977, Series B, 39(1):1-38. [4] Goodwin G C and Agero J C. Approximate EM algorithmsfor parameter and state estimation in nonlinear stochasticmodels. Proceedings of the 44th IEEE Conference onDecision and Control, and the European Control Conference2005. Seville, Spain, 2005: 368-373. [5] Lange K A. Gradient algorithm locally equivalent to the EMalgorithm. Journal of the Royal Statistical Society, 1995,Series B, 59(2): 425-437. [6] Berzuini C and Best N G, et al.. Dynamic conditionalindependence models and Markov chain Monte Carlomethods[J].Journal of the American Statistical Association.1997, 92(440):1403-1441 [7] Gordon N, Salmond D, and Smith A F M. Novel approach tononlinear and non-Gaussian Bayesian state estimation. IEEProceedings-F, 1993, 140(2): 107-113. [8] Liu J and West M. Combined parameter and state estimationin simulation-based filtering. in Sequential Monte Carlo inPractice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NewYork: Springer-Verlag, 2001: 197-223. [9] Storvik G. Particle filters in state space models with thepresence of unknown static parameters[J].IEEE Trans. onSignal Processing.2002, 50(2):281-289 [10] Wan E A and Nelson A T. Dual extended Kalman filtermethods. in Kalman Filtering and Neural Networks, S.Haykin, Eds. New York: John Wiley and Sons, Inc., 2001:123-173. [11] Arulampalam M S and Maskell S, et al.. A tutorial on particlefilters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Trans. on Signal Processing.2002, 50(2):174-188 [12] Minvielie P and Marrs A D, et al.. Joint target tracking andidentification: part I: sequential Monte Carlo model-basedapproaches. 8th International Conference on InformationFusion. Philadelphia, USA: FUSION'2005: 25-29. [13] Ristic B and Farina A, et al.. Performance bounds andcomparison of nonlinear filters for tracking a ballistic objecton re-entry[J].IEE Proceedings on Radar, Sonar andNavigation.2003, 150(2):65-70 [14] 帕普里斯A, 佩莱S. 保铮等译. 概率、随机变量与随机过程.西安：西安交通大学出版社，2004: 70-72. [15] Kay S M. 罗鹏飞，张文明等译. 统计信号处理基础估计与检测理论. 北京：电子工业出版社，2006: 85-102. [16] Anderson B and Moore J. Optimal Filtering. EnglewoodCliffs, NJ: Prentice-Hall. 1979: 193-222. [17] Kitagawa G. A nonlinear smoothing method for time seriesanalysis. Statistica Sinica, 1991, 1(2): 371-388. [18] Chen E J. Simulation-based estimation of quantiles.Proceedings of the 31st conference on Winter simulation,Arizona, United States, 1999: 428-434. [19] Athans R and Berolini A. Suboptimal state estimation forcontinuous-time nonlinear systems from discrete noisymeasurements[J].IEEE Trans. on Automatic Control.1968,13(5):504-514 [20] Diaz-Garcia J A and Jaimez R G. Noncentral matrix variatebeta distribution. available from http: // www.cimat.mx/reportes/enlinea/I-06-06.pdf. 2006.12. 24. [21] Wagle B. Multivariate beta distribution and a test formultivariate normality. Journal of the Royal StatisticalSociety, 1968, Series B, 30(3): 511-516.