[1] Kalman R E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 1960, 82(1): 35 -45[2] Alspach D L, Sorenson H W. Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control, 1972, 17(4): 439-448[3] Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space model. Journal of Computational and Graphical Statistics, 1996, 5(1): 1-25[4] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings F: Radar and Signal Processing, 1993, 140(2): 107-113[5] Doucet A, de Freitas N, Gordon N. Sequential Monte Carlo Methods in Practice. Heidelberg: Springer, 2001[6] Zuo Jun-Yi, Zhang Yi-Zhe, Liang Yan. Particle filter based on adaptive part resampling. Acta Automatica Sinica, 2012, 38(4): 647-652(左军毅, 张怡哲, 梁彦. 自适应不完全重采样粒子滤波器. 自动化学报, 2012, 38(4): 647-652)[7] Ouyang Cheng, Ji Hong-Bing, Guo Zhi-Qiang. Improved multiple model particle PHD and CPHD filters. Acta Automatica Sinica, 2012, 38(3): 341-348(欧阳成, 姬红兵, 郭志强. 改进的多模型粒子PHD和CPHD滤波算法. 自动化学报, 2012, 38(3): 341-348)[8] Wang Xiang-Hai, Fang Ling-Ling, Cong Zhi-Huan. Research on real-time multi-target tracking algorithm based on MSPF. Acta Automatica Sinica, 2012, 38(1): 139-144(王相海, 方玲玲, 丛志环. 基于MSPF的实时监控多目标跟踪算法研究. 自动化学报, 2012, 38(1): 139-144)[9] Yang Xiao-Jun, Xing Ke-Yi. Channel fault tolerant target tracking in multi-hop wireless sensor networks based on particle filtering. Acta Automatica Sinica, 2011, 37(4): 440- 448(杨小军, 邢科义. 无线多跳传感器网络下基于粒子滤波的信道容错的目标跟踪方法. 自动化学报, 2011, 37(4): 440-448)[10] Zhao Ling-Ling, Ma Pei-Jun, Su Xiao-Hong. A fast quasi-Monte Carlo-based particle filter algorithm. Acta Automatica Sinica, 2010, 36(9): 1351-1356(赵玲玲, 马培军, 苏小红. 一种快速准蒙特卡罗粒子滤波算法. 自动化学报, 2010, 36(9): 1351-1356)[11] Ye Long, Wang Jing-Ling, Zhang Qin. Genetic resampling particle filter. Acta Automatica Sinica, 2007, 33(8): 885- 887(叶龙, 王京玲, 张勤. 遗传重采样粒子滤波器. 自动化学报, 2007, 33(8): 885-887)[12] Kitagawa G. A self-organizing state-space model. Journal of the American Statistical Association, 1998, 93(443): 1203- 1215[13] Hüseler M, Künsch H R. Approximating and maximizing the likelihood for a general state-space model. Sequential Monte Carlo Methods in Practice. Heidelberg: Springer, 2001. 159-175[14] Yano K. A self-organizing state space model and simplex initial distribution search. Computational Statistics, 2008, 23(2): 197-216[15] Deb K. A population-based algorithm-generator for real-parameter optimization. Soft Computing, 2005, 9(4): 236- 253[16] Cai Z X, Wang Y. A multiobjective optimization-based evolutionary algorithm for constrained optimization. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658-675[17] Peng H, Ozaki T, Haggan-Ozaki V. Modeling and asset allocation for financial markets based on a discrete time microstructure model. The European Physical Journal B: Condensed Matter, 2003, 31(2): 285-293