[1]张 磊,郭 健,钱 晨,等.基于变分贝叶斯的容积平滑变结构滤波[J].南京理工大学学报(自然科学版),2019,43(03):255-260.
　Zhang Lei,Guo Jian,Qian Chen,et al.Variational Bayesian based cubature smooth variable structure filter[J].Journal of Nanjing University of Science and Technology,2019,43(03):255-260.
点击复制

基于变分贝叶斯的容积平滑变结构滤波()
分享到：

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年03期
页码:
255-260
栏目:
出版日期:
2019-06-30

文章信息/Info

Title:
Variational Bayesian based cubature smooth variable structure filter
文章编号:
1005-9830(2019)03-0255-06
作者:
张 磊; 郭 健; 钱 晨; 陈庆伟
南京理工大学 自动化学院,江苏 南京 210094
Author(s):
Zhang Lei; Guo Jian; Qian Chen; Chen Qingwei
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
变分贝叶斯;  平滑变结构滤波;  非线性状态估计;  容积变换
Keywords:
variational Bayesian;  smooth variable structure filter;  nonlinear state estimation;  cubature transform
分类号:
TP273
摘要:
为了减小建模误差和未知量测噪声特性对非线性状态估计的影响,该文提出了1种新的容积平滑变结构滤波算法。融合了非线性容积变换规则,可避免线性化误差。利用滑模变结构思想计算最优平滑边界层,约束建模误差的影响。利用变分贝叶斯实时估计动态系统的量测噪声特性,有助于优化平滑边界层的阈值。仿真结果表明,相比传统非线性滤波算法,该文算法精度可提高28.5%,具有更好的滤波性能。
Abstract:
A new cubature smooth variable structure filter algorithm is proposed to decrease the effect of modelling error and unknown measurement noise characteristic on nonlinear state estimation. The nonlinear cubature transform rule is combined to avoid the linearization error. Through the sliding mode variable structure idea,the optimal smoothing boundary layer is calculated to restrict the modelling error. The variational Bayesian is used to estimate the real-time measurement noise characteristic in the dynamic system,and the threshold value of smoothing boundary layer is optimized. The simulation results demonstrate that compared with the traditional nonlinear filter algorithm,the proposed algorithm improves the accuracy by 28.5%,and shows a better filter performance.

参考文献/References:

[1] 刘华军,赖少发. 汽车毫米波雷达目标跟踪的快速平方根CKF算法[J]. 南京理工大学学报,2016,40(1):56-60.
Liu Huajun,Lai Shaofa. Fast square root CKF for automotive millimeter-wave radar target tracking[J]. Journal of Nanjing University of Science and Technology,2016,40(1):56-60.
[2]Nguyen N H,DoAgˇan?ay K. Improved pseudolinear Kalman filter algorithms for bearings-only target tracking[J]. IEEE Transactions on Signal Processing,2017,65(23):6119-6134.
[3]杨春,郭健,张磊,等. 采用无迹信息滤波的多传感器容错融合算法[J]. 南京理工大学学报,2017,41(3):269-277.
Yang Chun,Guo Jian,Zhang Lei,et al. Multi-sensor fault-tolerant fusion algorithm using unscented information filter[J]. Journal of Nanjing University of Science and Technology,2017,41(3):269-277.
[4]Zhao Fangfang,Ge Shuzhi,Zhang Jie,et al. Celestial navigation in deep space exploration using spherical simplex unscented particle filter[J]. IET Signal Processing,2018,12(4):463-470.
[5]Zhao Yingwei. Cubature+extended hybrid Kalman filtering method and its application in PPP/IMU tightly coupled navigation systems[J]. IEEE Sensors Journal,2015,15(12):6973-6985.
[6]Paschero M,Storti G L,Rizzi A,et al. A novel mechanical analogy-based battery model for SoC estimation using a multicell EKF[J]. IEEE Transac-tions on Sustainable Energy,2016,7(4):1695-1702.
[7]Allotta B,Caiti A,Costanzi R,et al. A new AUV navigation system exploiting unscented Kalman filter[J]. Ocean Engineering,2016,113(2):121-132.
[8]Zhang Lei,Li Sheng,Zhang Enze,et al. Robust measure of non-linearity-based cubature Kalman filter[J]. IET Science Measurement & Technology,2017,11(7):929-938.
[9]Jia Bin,Xin Ming,Cheng Yang. Anisotropic sparse Gauss-Hermite quadrature filter[J]. Journal of Guidance Control & Dynamics,2012,35(3):1014-1023.
[10]Jia Bin,Xin Ming. Vision-based spacecraft relative navigation using sparse-grid quadrature filter[J]. IEEE Transactions on Control Systems Technology,2013,21(5):1595-1606.
[11]Li Wenling,Jia Yingmin. Location of mobile station with maneuvers using an IMM-based cubature Kalman filter[J]. IEEE Transactions on Industrial Electronics,2012,59(11):4338-4348.
[12]Li Ning,Zhu Ruihui,Zhang Yonggang. A strong tracking square root CKF algorithm based on multiple fading factors for target tracking[C]//7th International Joint Conference on Computational Sciences and Optimization. Beijing,China:IEEE,2014:16-20.
[13]Gadsden S A,Al-Shabi M,Arasaratnam I,et al. Combined cubature Kalman and smooth variable structure filtering:A robust nonlinear estimation strategy[J]. Signal Processing,2014,96(5):290-299.
[14]Koutsourelakis P S. Variational Bayesian strategies for high-dimensional,stochastic design problems[J]. Journal of Computational Physics,2016,308(3):124-152.
[15]Li Kailong,Chang Lubin,Hu Baiqing. A variational Bayesian based unscented Kalman filter with both adaptivity and robustness[J]. IEEE Sensor Journal,2016,16(18):6966-6976.
[16]Gadsden S A,Habibi S,Kirubarajan T. Kalman and smooth variable structure filters for robust estimation[J]. IEEE Transactions on Aerospace & Electronic Systems,2014,50(2):1038-1050.
[17]Jia Bin,Xin Ming,Cheng Yang. High-degree cubature Kalman filter[J]. Automatica,2013,49(2):510-518.
[18]Huang Yulong,Zhang Yonggang,Wu Zhemin,et al. A novel adaptive Kalman Filter with inaccurate process and measurement noise covariance matrices[J]. IEEE Transactions on Automatic Control,2017,63(2):594-601.
[19]Habibi S. The smooth variable structure filter[J]. Proceedings of the IEEE,2007,95(5):1026-1059.
[20]林孝工,焦玉召,李恒,等. 基于容积变换的平滑变结构滤波及应用[J]. 系统工程与电子技术,2018,40(1):159-164.
Lin Xiaogong,Jiao Yuzhao,Li Heng,et al. Smoothing variable structure filter based on cubature transform and its application[J]. Systems Engineering and Electronics,2017,40(1):159-164.
[21]Jia Bin,Xin Ming. Adaptive cubature Kalman filter with directional uncertainties[J]. IEEE Transactions on Aerospace & Electronic Systems,2016,52(3):1477-1486.

备注/Memo

备注/Memo:
收稿日期:2018-08-08 修回日期:2018-10-15
基金项目:国家自然科学基金(61673214; 61673217; 61673219); 江苏省研究生创新基金(KYLX16_0450)
作者简介:张磊(1990-),男,博士生,主要研究方向:非线性滤波、组合导航系统等,E-mail:zhanglei002330@163.com; 通讯作者:郭健(1974-),男,教授,博士生导师,主要研究方向:智能控制与智能系统、导航制导等,E-mail:guoj1002@njust.edu.cn。
引文格式:张磊,郭健,钱晨,等. 基于变分贝叶斯的容积平滑变结构滤波[J]. 南京理工大学学报,2019,43(3):255-260.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-06-30