1 |
Wang D J, Lv H F, Wu J. Augmented cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise[J]. Measurement, 2017, 97: 111-125.
|
2 |
Huang Y L, Zhang Y G. A new process uncertainty robust student’s t based Kalman filter for SINS/GPS integration[J]. IEEE Access, 2017, 5: 14391-14404.
|
3 |
Huang W, Xie H S, Shen C, et al. A robust strong tracking cubature Kalman filter for spacecraft attitude estimation with quaternion constraint[J]. Acta Astronautica, 2016, 121: 153-163.
|
4 |
Nurminen H, Ardeshiri T, Piche R, al el. Robust inference for state-space models with skewed measurement noise[J]. IEEE Signal Processing Letters, 2015, 22(11): 1898-1902.
|
5 |
Hawes M, Amer H M, Mihaylova L. Traffic state estimation via a particle filter over a reduced measurement space[A]. 20th International Conference on Information Fusion[C]. Xi’an, China: IEEE, 2017. 1-8.
|
6 |
Cowpertwait P S P, Metcalfe A V. Introductory Time Series with R[M]. New York, USA: Springer-Verlag, 2009.
|
7 |
Costa O L V, Fragoso M D, Marques R P. Discrete-Time Markov Jump Linear Systems[M]. London,UK: Springer-Verlag, 2005.
|
8 |
Fang Y G, Loparo K A. Stochastic stability of jump linear systems[J]. IEEE Transactions on Automatic Control, 2002, 47(7): 1204-1208.
|
9 |
Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V: multiple-model methods[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321.
|
10 |
Magill D. Optimal adaptive estimation of sampled stochastic processes[J]. IEEE Transactions on Automatic Control, 1965, 10(4): 434-439.
|
11 |
Chang C B, Athans M. State estimation for discrete systems with switching parameters[J]. IEEE Transactions on Aerospace and Electronic Systems, 1978, 14(3): 418-425.
|
12 |
Blom H A P, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Transactions on Automatic Control, 1988, 33(8): 780-783.
|
13 |
Doucet A, Gordon N J, Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems[J]. IEEE Transactions on Signal Processing, 2001, 49(3): 613-624.
|
14 |
Blom H A P, Bloem E A. Exact Bayesian and particle filtering of stochastic hybrid systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 55-70.
|
15 |
梁彦, 程咏梅, 贾宇岗, 等. 交互式多模型算法性能分析[J]. 控制理论与应用, 2001, 18(4): 487-492.
|
|
Liang Y, Cheng Y M, Jia Y G, et al. Analysis on the performance and properties of interacting multiple models algorithm[J]. Control Theory and Applications, 2001, 18(4): 487-492. (in Chinese)
|
16 |
Li W L, Jia Y M. An information theoretic approach to interacting multiple model estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1811-1825.
|
17 |
王树亮, 毕大平, 阮怀林, 等. 基于信息熵准则的认知雷达机动目标跟踪算法[J]. 电子学报, 2019, 47(6): 1277-1284.
|
|
Wang S L, Bi D P, Ruan H L, et al. Cognitive radar maneuvering target tracking algorithm based on information entropy criterion[J]. Acta Electronica Sinica, 2019, 47(6): 1277-1284. (in Chinese)
|
18 |
Johnston L A, Krishnamurthy V. An improvement to the interacting multiple model (IMM) algorithm[J]. IEEE Transactions on Signal Processing, 2001, 49(12): 2909-2923.
|
19 |
许登荣,程水英,包守亮. 自适应转移概率交互式多模型跟踪算法[J]. 电子学报, 2017, 45(9): 2113-2120.
|
|
Xu D R, Cheng S Y, Bao S L. Interacting multiple model algorithm based on adaptive transition probability[J]. Acta Electronica Sinica, 2017, 45(9): 2113-2120. (in Chinese)
|
20 |
臧荣春, 崔平远. 马尔可夫参数自适应IFIMM算法研究[J]. 电子学报, 2006, 34(3): 521-524.
|
|
Zang R C, Cui P Y. Research on adaptive Markov parameter IFIMM algorithm[J]. Acta Electronica Sinica, 2006, 34(3): 521-524. (in Chinese)
|
21 |
Lan J, Li X R, Mu C D. Best model augmentation for variable-structure multiple-model estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 2008-2025.
|
22 |
Principe J C. Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives[M]. New York,USA: Springer-Verlag, 2010.
|
23 |
Liu W, Pokharel P P, Principe J C. Correntropy: properties and applications in non-Gaussian signal processing[J]. IEEE Transactions on Signal Processing, 2007, 55(11): 5286-5298.
|
24 |
Wang F, He Y C, Wang S Y, et al. Maximum total correntropy adaptive filtering against heavy-tailed noises[J]. Signal Processing, 2017, 141: 84-95.
|
25 |
Ma W T, Qu H, Gui G, et al. Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments[J]. Journal of the Franklin Institute, 2015, 352: 2708-2727.
|
26 |
Peng S Y, Chen B D, Sun L, al el. Constrained maximum correntropy adaptive filtering[J]. Signal Processing, 2017, 140: 116-126.
|
27 |
Chen B D, Liu X, Zhao H Q, et al. Maximum correntropy Kalman filter[J]. Automatica, 2017, 76: 70-77.
|
28 |
Liu X, Chen B D, Xu B, et al. Maximum correntropy unscented filter[J]. International Journal of Systems Science, 2017, 48(8): 1607-1615.
|
29 |
Liu X, Qu H, Zhao J H, et al. Extended Kalman filter under maximum correntropy criterion[A]. Proceedings of International Joint Conference on Neural Networks[C]. Vancouver, Canada: IEEE, 2016.1733-1737.
|
30 |
Wang G Q, Li N, Zhang Y G. Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise[J]. Journal of the Franklin Institute, 2017, 354: 8659-8677.
|
31 |
Liu X, Qu H, Zhao J H, et al. Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems[J]. ISA Transactions, 2019, 80: 195-202.
|
32 |
Liu D, Chen X Y, Xu Y, et al. Maximum correntropy generalized high-degree cubature Kalman filter with application to the attitude determination system of missile[J]. Aerospace Science and Technology, 2019, 95: 105441.
|
33 |
Zhou W D, Liu M M. Robust interacting multiple model algorithms based on multi-sensor fusion criteria[J]. International Journal of Systems Science, 2015, 47(1): 92-106.
|
34 |
Li D, Sun J. Robust interacting multiple model filter based on Student’s t-distribution for heavy-tailed measurement noises[J]. Sensors, 2019, 19(22): 4830.
|
35 |
Chen B D, Wang X, Lu N, et al. Mixture correntropy for robust learning[J]. Pattern Recognition, 2018, 79: 318-327.
|
36 |
Battistelli G, Chisci L. Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability[J]. Automatica, 2014, 50: 707-718.
|
37 |
戴定成, 姚敏立, 蔡宗平, 等. 改进的马尔可夫参数自适应IMM算法[J]. 电子学报, 2017, 45(5): 1198-1205.
|
|
Dai D C, Yao M L, Cai Z P, et al. Improved adaptive Markov IMM algorithm[J]. Acta Electronica Sinica, 2017, 45(5): 1198-1205. (in Chinese)
|
38 |
Seah C E, Hwang I. Algorithm for performance analysis of the IMM algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 1114-1124.
|