通信工程、自动化技术 |
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带测量偏置估计的鲁棒卡尔曼滤波算法 |
朱光明1,2, 蒋荣欣1,2, 周凡1,2, 田翔1,2, 陈耀武1,2 |
1. 浙江大学 数字技术及仪器研究所,浙江 杭州 310027; 2. 浙江省网络多媒体技术研究重点实验室,浙江 杭州 310027 |
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Robust Kalman filtering algorithm with estimation of measurement biases |
ZHU Guang-ming1,2, JIANG Rong-xin1,2, ZHOU Fan1,2, TIAN Xiang1,2, CHEN Yao-wu1,2 |
1. Institute of Advanced Digital Technology and Instrumentation, Zhejiang University, Hangzhou 310027, China; 2. Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Hangzhou 310027, China |
引用本文:
朱光明, 蒋荣欣, 周凡, 田翔, 陈耀武. 带测量偏置估计的鲁棒卡尔曼滤波算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2015.07.020.
ZHU Guang-ming, JIANG Rong-xin, ZHOU Fan, TIAN Xiang, CHEN Yao-wu. Robust Kalman filtering algorithm with estimation of measurement biases. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2015.07.020.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2015.07.020
或
http://www.zjujournals.com/eng/CN/Y2015/V49/I7/1343
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[1] WELCH G, BISHOP G. An introduction to Kalman filtering [R]. Chapel Hill, NC: University of North Carolina, 2006.
[2] RABINER L, JUANG B. An introduction to hidden Markov models [J]. IEEE ASSP Magazine, 1986, 3(1): 416.
[3] ZHU H, LEUNG H, HE Z. A variational Bayesian approach to robust sensor fusion based on Student-t distribution [J]. Information Sciences, 2013, 221(0): 201-214.
[4] JO-ANNE T, THEODOROU E, SCHAAL S. A Kalman filter for robust outlier detection [C]∥ IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego: IEEE, 2007: 1514-1519.
[5] SARKKA S, NUMMENMAA A. Recursive noise adaptive Kalman filtering by variational Bayesian approximations [J]. IEEE Transactions on Automatic Control, 2009, 54(3): 596-600.
[6] AGAMENNONI G, NIETO J I, NEBOT E M. An outlier-robust Kalman filter [C]∥ IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 1551-1558.
[7] AGAMENNONI G, NIETO J I, NEBOT E M. Approximate inference in state-space models with heavy-tailed noise [J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5024-5037.
[8] SARKKA S, HARTIKAINEN J. Non-linear noise adaptive Kalman filtering via variational Bayes [C]∥ IEEE International Workshop on Machine Learning for Signal Processing. Southampton: IEEE, 2013: 16.
[9] BEAL M J. Variational algorithms for approximate Bayesian inference [D]. London: University of London, 2003.
[10] OKELLO N, RISTIC B. Maximum likelihood registration for multiple dissimilar sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3): 1074-1083.
[11] TAGHAVI E, THARMARASA R, KIRUBARAJAN T, et al. Bias estimation for practical distributed multiradar-multitarget tracking systems [C]∥ 16th International Conference on Information Fusion. Istanbul: ISIF, 2013: 1304-1311.
[12] GELMAN A, CARLIN J B, STERN H S, et al. Bayesian data analysis [M]. Florida: CRC, 2013.
[13] OZKAN E, SMIDL V, SAHA S, et al. Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters [J]. Automatica, 2013, 49(6): 1566-1575.
[14] HERSHEY J R, OLSEN P A. Approximating the Kullback Leibler divergence between Gaussian mixture models [C]∥ IEEE International Conference on Acoustics, Speech and Signal Processing. Hawaii: IEEE, 2007: 317-320.
[15] ARASARATNAM I, HAYKIN S. Cubature Kalman filters [J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
[16] COWLES M K. Applied Bayesian statistics [M]. New York: Springer, 2013. |
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