[1] Arasaratnam I, Haykin S. Cubature Kalman filter. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269
[2] [2] Jia B, Xin M, Cheng Y. High-degree cubature Kalman filter. Automatica, 2013, 49(2): 510-518
[3] Zhang Yong-Gang, Huang Yu-Long, Wu Zhe-Min, Li Ning. A high order unscented Kalman filtering method. Acta Automatica Sinica, 2014, 40(5): 838-848(张勇刚, 黄玉龙, 武哲民, 李宁. 一种高阶无迹卡尔曼滤波方法. 自动化学报, 2014, 40(5): 838-848)
[4] [4] Jia B, Xin M, Cheng Y. Sparse-grid quadrature nonlinear filtering. Automatica, 2012, 48(2): 327-341
[5] [5] Dunk J, Straka O, imandl M. Stochastic integration filter. IEEE Transactions on Automatic Control, 2013, 58(6): 1561-1566
[6] [6] Zhang X C. A novel cubature Kalman filter for nonlinear state estimation. In: Proceedings of the 52nd IEEE Conference on Decision and Control. Florence, Italy: IEEE, 2013. 7797-7802
[7] [7] Wang S Y, Feng J C, Tse C K. Spherical simplex-radial cubature Kalman filter. IEEE Signal Processing Letters, 2014, 21(1): 43-46
[8] Wang Lu, Li Guang-Chun, Qiao Xiang-Wei, Wang Zhao-Long, Ma Tao. An adaptive UKF algorithm based on maximum likelihood principle and expectation maximization algorithm. Acta Automatica Sinica, 2012, 38(7): 1200-1210(王璐, 李光春, 乔相伟, 王兆龙, 马涛. 基于极大似然准则和最大期望算法的自适应UKF算法. 自动化学报, 2012, 38(7): 1200-1210)
[9] [9] Chang L B, Hu B Q, Li A, Qin F J. Transformed unscented Kalman Filter. IEEE Transactions on Automatic Control, 2013, 58(1): 252-257
[10] Schn T B, Wills A, Ninness B. System identification of nonlinear state-space models. Automatica, 2011, 47(1): 39-49
[11] Tichavsky P, Muravchik C H, Nehorai A. Posterior Cram'er-Rao bounds for discrete-time nonlinear filtering. IEEE Transactions on Signal Processing, 1998, 46(5): 1386-1396
[12] Zuo L, Niu R X, Varshney P K. Conditional posterior Cramr-Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Transactions on Signal Processing, 2011, 59(1): 1-14
[13] Zheng Y J, Ozdemir O, Niu R X, Varshney P K. New conditional posterior Cramr-Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Transactions on Signal Processing, 2012, 60(10): 5549-5556
[14] Zuo L. Conditional Posterior Cramr-Rao Lower Bound and Distributed Target Tracking in Sensor Networks [Ph.D. dissertation], Syracuse University, Syracuse, USA, 2011.
[15] Yang F W, Wang Z D, Feng G, Liu X H. Robust filtering with randomly varying sensor delay: the finite-horizon case. IEEE Transactions on Circuits and Systems I, 2009, 56(3): 664-672
[16] Chen S J, Li Y Y, Qi G Q, Sheng A D. Adaptive Kalman estimation in target tracking mixed with random one-step delays, stochastic-bias measurements, and missing measurements. Discrete Dynamics in Nature and Society, 2013, 2013: Article ID 716915
[17] Hermoso-Carazo A, Linares-Prez J. Extended and unscented filtering algorithms using one-step randomly delayed observations. Applied Mathematics and Computation, 2007, 190(2): 1375-1393
[18] Hermoso-Carazo A, Linares-Prez J. Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems. Applied Mathematical Modelling, 2009, 33(9): 3705-3717
[19] Wang X X, Liang Y, Pan Q, Zhao C H. Gaussian filter for nonlinear systems with one-step randomly delayed measurements. Automatica, 2013, 49(4): 976-986
[20] Wang X X, Pan Q, Liang Y, Yang F. Gaussian smoothers for nonlinear systems with one-step randomly delayed measurements. IEEE Transactions on Automatic Control, 2013, 58(7): 1828-1835
[21] Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 2004, 49(9): 1453-1461
[22] Zhou T. Robust recursive state estimation with random measurements droppings. available at arXiv: 1401.4020v1 [cs.SY], 2014.
[23] Ray A. Output feedback control under randomly varying distributed delays. Journal of Guidance, Control, and Dynamics, 1994, 17(4): 701-711
[24] Horn R, Johnson C R. Matrix Analysis. New York: Cambridge University Press, 1985.
[25] Ito K, Xiong K Q. Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control, 2000, 45(5): 910-927
[26] Bucy R S, Senne K D. Digital synthesis of non-linear filters. Automatica, 1971, 7(3): 287-298
[27] Dunik J, Simandl M, Straka O. Unscented Kalman filter: aspects and adaptive setting of scaling parameter. IEEE Transactions on Automatic Control, 2012, 57(9): 2411-2416
[28] Li W L, Jia Y M. H filtering for a class of nonlinear discrete-time systems based on unscented transform. Signal Processing, 2010, 90(12): 3301-3307
[29] Jia B, Xin M. Sparse-grid quadrature H filter for discrete-time systems with uncertain noise statistics. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 1626-1636