2017 03 v.40;No.273 64-68
卡尔曼滤波算法研究
基金项目(Foundation):
邮箱(Email):
DOI:
10.16426/j.cnki.jcdzdk.2017.03.015
中文作者单位:
中国电子科技集团公司第五十一研究所;
摘要(Abstract):
对卡尔曼滤波的起源和发展进行了简述,然后对标准卡尔曼滤波的定义和模型进行了回顾,重点对近似二阶扩展卡尔曼滤波、扩维无迹卡尔曼滤波和自适应卡尔曼滤波等3种最新改进型的卡尔曼滤波算法进行了详细阐述,最后对这3种新改进型的卡尔曼滤波算法的优缺点进行了对比分析,对各自的适用领域和场景进行了说明。
关键词(KeyWords):
卡尔曼滤波;;近似二阶扩展卡尔曼滤波;;无迹卡尔曼滤波;;自适应卡尔曼滤波
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参考文献
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[2]GAO S S,HU G G,ZHONG Y M.Windowing and random weighting-based adaptive unscented Kalman filter[J].International Journal of Adaptive Control and Signal Processing,2014,29(2):201-223.
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[4]MEDEIROS C B,WANDERLEY M M.Multiple-model linear Kalman filter framework for unpredictable signals[J].IEEE Sensors Journal,2014,14(4):979-991.
[5]CHANG G.Kalman filter with both adaptivity and robustness[J].Process Control,2014,24(3):81-87.
[6]CHANG L,HU B,LI A,QIN F.Transformed unscented Kalman filter[J].IEEE Transactions on Automatic Control,2013,58(1):252-257.
[7]HORWOOD J T,POORE A B.Adaptive Gaussian sum filtersfor space surveillance[J].IEEE Transactions on Automatic Control,2011,56(8):1777-1790.
[8]HUR H,AHN H S.Discrete-time H∞filtering for mobile robotlocalization using wireless sensor network[J].IEEE Sensors Journal,2013,13(1):245-252.
[9]KARLGAARD C D.Nonlinear regression Huber-Kalman filtering andfixed-interval smoothing[J].Journal of Guidance,Control and Dynamics,2014,38(2):322-330.
[10]MBALAWATA I S,SRKKS,VIHOLA M,HAARIO H.Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter[J].Computer Statist and Data Analysis,2015,83(1):101-115.
[2]GAO S S,HU G G,ZHONG Y M.Windowing and random weighting-based adaptive unscented Kalman filter[J].International Journal of Adaptive Control and Signal Processing,2014,29(2):201-223.
[3]GUSTAFSSON F,HENDEBY G.Some relations between extended and unscented Kalman filters[J].IEEE Transactions on Signal Process,2012,60(2):545-555.
[4]MEDEIROS C B,WANDERLEY M M.Multiple-model linear Kalman filter framework for unpredictable signals[J].IEEE Sensors Journal,2014,14(4):979-991.
[5]CHANG G.Kalman filter with both adaptivity and robustness[J].Process Control,2014,24(3):81-87.
[6]CHANG L,HU B,LI A,QIN F.Transformed unscented Kalman filter[J].IEEE Transactions on Automatic Control,2013,58(1):252-257.
[7]HORWOOD J T,POORE A B.Adaptive Gaussian sum filtersfor space surveillance[J].IEEE Transactions on Automatic Control,2011,56(8):1777-1790.
[8]HUR H,AHN H S.Discrete-time H∞filtering for mobile robotlocalization using wireless sensor network[J].IEEE Sensors Journal,2013,13(1):245-252.
[9]KARLGAARD C D.Nonlinear regression Huber-Kalman filtering andfixed-interval smoothing[J].Journal of Guidance,Control and Dynamics,2014,38(2):322-330.
[10]MBALAWATA I S,SRKKS,VIHOLA M,HAARIO H.Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter[J].Computer Statist and Data Analysis,2015,83(1):101-115.
基本信息:
DOI：10.16426/j.cnki.jcdzdk.2017.03.015
中图分类号:TN713
引用信息:
[1]毛秀华,吴健.卡尔曼滤波算法研究[J].舰船电子对抗,2017,40(03):64-68.DOI:10.16426/j.cnki.jcdzdk.2017.03.015.
基金信息:
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