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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。
Abstract：This paper expatiates the origin and development of Kalman filtering briefly,then reviews the definition and model of the standard Kalman filtering,focuses on expatiating three new improved Kalman fitering algorithms in detail:approximate two order extended Kalman filtering,augmented unscented Kalman filtering,adaptive Kalman filtering,finally compares and analyzes the advantages and disadvantages of three new improved Kalman filtering algorithms,performs illumination to respective application fields and scene.
[1]黄小平,王岩.卡尔曼滤波原理及应用[M].北京:电子工业出版社,2015.
[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: