非高斯环境下船变测量最大熵卡尔曼滤波方法

doi:10.12305/j.issn.1001-506X.2021.11.28

Abstract

Abstract:

In the process of hull deformation measurement based on inertial matching method, the conventional Kalman filter possibly causes a greater deviation or even divergence of filtering when the fiber gyroscope unit (FGU) is being exposed to a certain external environment under complex sea conditions. To solve this problem, this paper introduces a maximum correntropy criterion Kalman filter (MCCKF) used for environment under model-unknown or non-Gaussian noises based on weighted matrix, into which both the maximum correntropy criterion (MCC) and weighted least squares (WLS) are introduced to improve its iterative process. Finally, a series of comparative experiments based on angular velocity matching method are carried out. Compared with adaptive and traditional MCC Kalman filter (MCCKF) avoids timely modifying parameters and achieves more precise estimation of deformation, meets the requirements of rapid convergence, as well as stronger robustness of resisting complex disturbance.

Key words: hull deformation measurement, Kalman filter, non-Gaussian, maximum correntropy criterion (MCC), weighted least squares (WLS)

CLC Number:

TN13
TN96

Zixuan LONG, Qi ZHOU, Xiafu PENG, Xiaoli ZHANG. Maximum correntropy Kalman filter used for hull deformation measurement in non-Gaussian environment[J]. Systems Engineering and Electronics, 2021, 43(11): 3278-3287.

Figures/Tables 14

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Table 2

Fig.5

Fig.6

Fig.7

Table 3

Fig.8

Fig.9

Fig.10

Table 4

References 30

1	MOCHALOV A V, ROUMIANTSEV O A. Strapdown inertial navigation system on laser gyroscopes in the attitude definition mode[C]//Proc. of the 2nd International Conference on Lasers for Measurement and Information Transfer, 2002, 4680: 72-79.
2	史宏洋, 尤太华, 张义, 等. 基于角速率匹配的船体变形实船测量技术研究[J]. 船舶力学, 2017, 21 (4): 429- 436. doi: 10.3969/j.issn.1007-7294.2017.04.007
	SHI H Y , YOU T Y , ZHANG Y , et al. Research on ship deformation measurement technology based on angular rate matching[J]. Journal of Ship Mechanics, 2017, 21 (4): 429- 436. doi: 10.3969/j.issn.1007-7294.2017.04.007
3	XIA X, SUN Q, ZHANG Y, et al. Ship deformation measurement based on angular rate matching method and quasi-static model[C]//Proc. of the IEEE International Conference on Control, Automation and Information Sciences, 2016: 137-142.
4	郑佳兴, 秦石乔, 王省书, 等. 基于姿态匹配的船体变形测量方法[J]. 中国惯性技术学报, 2010, 18 (2): 175- 180.
	ZHENG J X , QIN S Q , WANG X S , et al. An improved method of ship deformation measurement based on attitude matching[J]. Journal of Chinese Inertial Technology, 2010, 18 (2): 175- 180.
5	秦石乔, 王省书, 黄宗升, 等. 考虑准静态缓变量的船体变形角测量[J]. 中国惯性技术学报, 2011, 19 (1): 6- 10.
	QIN S Q , WANG X S , HUANG Z S , et al. Ship hull angular deformation measurement taking slow-varying quasi-static component into account[J]. Journal of Chinese Inertial Technology, 2011, 19 (1): 6- 10.
6	彭侠夫, 何荧, 杨功流, 等. 基于姿态角匹配的无模型船体变形测量方法[P]. 福建省: CN108871322B, 2021-02-09.
	PENG X F, HE Y, YANG G L, et al. Model free hull deformation measurement method based on attitude angle matching[P]. Fujian: CN108871322B, 2021-02-09.
7	HE Y , ZHANG X L , PENG X F . Research on hull deformation measurement for large azimuth misalignment angle based on attitude quaternion[J]. Optik, 2019, 182, 159- 169. doi: 10.1016/j.ijleo.2018.11.023
8	HE Y , ZHANG X L , PENG X F . A model-free hull deformation measurement method based on attitude quaternion matching[J]. IEEE Access, 2018, 6, 8864- 8869. doi: 10.1109/ACCESS.2018.2807183
9	PLATANIONTIS K N , ANDROUTSOS D , VENETSANOPOULOS A . Nonlinear filtering of non-Gaussian noise[J]. Journal of Intelligent and Robotic Systems, 1997, 2 (1): 207- 231.
10	孙昌跃, 邓正隆. 舰体挠曲运动在线建模研究[J]. 系统工程与电子技术, 2007, 29 (2): 243- 245. doi: 10.3321/j.issn:1001-506X.2007.02.022
	SUN C Y , DENG Z L . Research on the ship flexure on line modeling[J]. Systems Engineering and Electronics, 2007, 29 (2): 243- 245. doi: 10.3321/j.issn:1001-506X.2007.02.022
11	张涛, 王帅, 刘兴华. 一种改进的姿态匹配船体变形测量方法[J]. 中国惯性技术学报, 2020, 28 (1): 8- 14.
	ZHANG T , WANG S , LIU X H . An improved method of ship deformation measurement based on attitude matching[J]. Journal of Chinese Inertial Technology, 2020, 28 (1): 8- 14.
12	苏宛新, 黄春梅, 刘培伟, 等. 自适应Kalman滤波在SINS初始对准中的应用[J]. 中国惯性技术学报, 2010, 18 (1): 44- 47.
	SU W X , HUANG C M , LIU P W , et al. Application of adaptive Kalman filter technique in initial alignment of inertial navigation system[J]. Journal of Chinese Inertial Technology, 2010, 18 (1): 44- 47.
13	何昆鹏, 王晓雪, 王刚, 等. 改进的自适应卡尔曼滤波在SINS初始对准中的应用[J]. 兵工自动化, 2015, 34 (1): 65- 70.
	HE K P , WANG X X , WANG G , et al. Application of improved adaptive Kalman filter algorithm in SINS initial alignment[J]. Ordnance Industry Automation, 2015, 34 (1): 65- 70.
14	徐定杰, 贺瑞, 沈锋, 等. 基于新息协方差的自适应渐消卡尔曼滤波器[J]. 系统工程与电子技术, 2011, 33 (12): 2696- 2699.
	XU D J , HE R , SHEN F , et al. Adaptive fading Kalman filter based on innovation covariance[J]. Systems Engineering and Electronics, 2011, 33 (12): 2696- 2699.
15	钱华明, 葛磊, 彭宇. 多渐消因子卡尔曼滤波及其在SINS初始对准中的应用[J]. 中国惯性技术学报, 2012, 20 (3): 287- 291.
	QIAN H M , GE L , PENG Y . Multiple fading factors Kalman filter and its application in SINS initial alignment[J]. Journal of Chinese Inertial Technology, 2012, 20 (3): 287- 291.
16	薛海建, 郭晓松, 周召发. 基于自适应多重渐消因子卡尔曼滤波的SINS初始对准方法[J]. 系统工程与电子技术, 2017, 39 (3): 620- 626.
	XUE H J , GUO X S , ZHOU Z F . SINS initial alignment method based on adaptive multiple fading factors[J]. Systems Engineering and Electronics, 2017, 39 (3): 620- 626.
17	GAO W X , MIAO L J , NI M L . Multiple fading factors Kalman filter for SINS static alignment application[J]. Chinese Journal of Aeronautics, 2011, 24, 476- 483.
18	高伟, 李敬春, 奔粤阳, 等. 基于多重渐消因子的自适应卡尔曼滤波器[J]. 系统工程与电子技术, 2014, 36 (7): 1405- 1409.
	GAO W , LI J C , BEN Y Y , et al. Adaptive Kalman filter based on multiple fading factors[J]. Systems Engineering and Electronics, 2014, 46 (7): 1405- 1409.
19	郭士荦, 吴苗, 许江宁, 等. 自适应渐消卡尔曼滤波及其在SINS初始对准中的应用[J]. 武汉大学学报, 2018, 43 (11): 1667- 1672, 1680.
	GUO S L , WU M , XU J N , et al. Adaptive fading Kalman filter and its application in SINS initial alignment[J]. Geomatics and Information Science of Wuhan University, 2018, 43 (11): 1667- 1672, 1680.
20	ZHA F , GUO S L , LI F . An improved nonlinear filter based on adaptive fading factor applied in alignment of SINS[J]. International Journal for Light and Electron Optics, 2019, 184, 165- 176.
21	PRINCIPE J C . Information theoretical learning: Renyi's entropy and kernel perspectives[M]. New York: Springer Scien-ce+Business Media, 2010: 60- 128.
22	CHEN B D , ZHU Y , HU C J , et al. Principe system parameter identification: information criteria and algorithms[M]. London: Elsevier, 2013: 111- 120.
23	CINAR G T, PRINCIPE J C. Hidden state estimation using the correntropy filter with fixed point update and adaptive kernel size[C]//Proc. of the IEEE World Congress on Computational Intelligence, 2012.
24	REZA I, SEYED A F, HADI S Y, et al. Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise[C]//Proc. of the Annual Conference on Information Science and Systems, 2016.
25	LIU X, QU H, ZHAO J H, et al. Extended Kalman filter under maximum correntropy criterion[C]//Proc. of the International Joint Conference on Neural Networks, 2016.
26	LIU X , CHEN B D , XU B , et al. Maximum correntropy unscented filter[J]. International Journal of Systems Science, 2017, 48 (8): 1607- 1615.
27	SHEN T Y , REN W J , HAN M , et al. Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction[J]. Engineering Applications of Artificial Intelligence, 2020, 95, 103797- 103805.
28	SHI W L , LI Y S , WANG Y Y . Noise-free maximum correntropy criterion algorithm in non-Gaussian environment[J]. IEEE Trans.on Circuits and Systems Ⅱ, 2020, 67 (10): 2224- 2228.
29	SHI W L , LI Y S , CHEN B D . A separable maximum correntropy adaptive algorithm[J]. IEEE Trans.on Circuits and Systems Ⅱ, 2020, 67 (11): 2797- 2801.
30	KHODER M, NOURDINE A, MAAN N, et al. Combination of maximum correntropy criterion & α-Rényi divergence for a robust and fail-safe multi-sensor fata fusion[C]//Proc. of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2020: 61-67.

参数变量	设定值
离散步长	滤波时长2 h(即静态变形震荡周期的2倍), 采样频率100 Hz。
船舶激励	正弦运动: 俯仰角、横摇角和航向角幅值4°、5°、3°; 摇摆周期8 s、7 s、6 s; 初始相位均为0°。
安装误差	FGU1和FGU2之间的安装误差角为: [0.08°, 0.06°, 0.04°]。
静态变形	正弦信号模拟: 幅值均为0.1°; 频率均为2π/3 600;初始相位均为0°; 角速度均方差9×10^－6 rad/s
动态变形	二阶马尔可夫: 动态不规则系数μ_i分别为0.1、0.062 5、0.062 5, 单位1/s; 动态变形方差D_i分别为4.759 8×10^-8、2.115 3×10^-8、5.876 3×10^-8, 单位rad²; 动态变形主频率λ_i分别为0.13、0.1、0.13, 单位Hz; 标准高斯白噪声ω_i, b_i²=μ_i²+λ_i²。
陀螺漂移	常值漂移: 均为ε_jc =0.005°/h。随机漂移: 随机漂移不规则系数ζ_j=1/300, 随机漂移均方差离散程度σ_j =0.01°/h。

方向	算法
方向	MCKF	MAKF	TAKF	MCCKF
X轴	-	1.582 9	1.341 2	1.948 4
Y轴	-	9.866 0	3.399 5	3.820 7
Z轴	-	2.598 2	2.476 6	2.815 0

方向	算法
方向	MCKF	MAKF	TAKF	MCCKF
X轴	-	10.048 1	9.027 9	8.250 6
Y轴	-	11.658 0	11.071 7	10.292 2
Z轴	-	10.420 3	9.921 5	9.125 0

方向	算法
方向	MCKF	MAKF	TAKF	MCCKF
X轴	-	4.959 5	1.722 7	3.360 5
Y轴	-	7.831 9	2.917 8	3.509 7
Z轴	-	8.811 7	2.240 7	3.220 2

[1]	Geng XU, Yongxu HE, Yonggang ZHANG. Inertial-frame-based transfer alignment using Rodriguez parameters [J]. Systems Engineering and Electronics, 2022, 44(9): 2903-2913.
[2]	Haoran SHI, Faxing LU, Jiangxin QI, Guang YANG. Cooperative target tracking of UAVs based on aided beacon [J]. Systems Engineering and Electronics, 2022, 44(7): 2302-2310.
[3]	Guang ZHAI, Yanxin WANG, Yiyong SUN. Cooperative tracking filtering technology of multi-target based on low orbit satellite constellation [J]. Systems Engineering and Electronics, 2022, 44(6): 1957-1967.
[4]	Yiping DONG, Ning LIU, Zhong SU, Jingxiao WANG, Hongyang BAI. Integrated navigation method of high-speed spinning flying bodybased on AEKF [J]. Systems Engineering and Electronics, 2022, 44(6): 1977-1983.
[5]	Wenhua LI, Lixin WANG, Qiang SHEN, Can LI, Zongshou WU. MEMS-INS/GNSS/VO integrated navigation method based on robust EKF [J]. Systems Engineering and Electronics, 2022, 44(6): 1994-2000.
[6]	Qi WANG, Zhizhong LIAO, Fei YAN. Algorithm for countering velocity gate pull-off jamming of radar seeker based on probability data association [J]. Systems Engineering and Electronics, 2022, 44(2): 448-454.
[7]	Yi LIU, Xiaoxiong ZHOU, Guangjun CHENG. High dynamic carrier tracking technology in frequency hopping systems [J]. Systems Engineering and Electronics, 2022, 44(2): 677-683.
[8]	Zhaoqiang SUN, Zhigui WANG, Fei MENG, Luyu LI, Zhong YU, Yan CHEN. Ballistic target tracking filter design based on EKF and ballistic equations [J]. Systems Engineering and Electronics, 2022, 44(10): 3207-3212.
[9]	Pingan ZHANG, Wei WANG, Min GAO, Yi WANG. Research on SR-CH∞KF for projectile attitude measurement [J]. Systems Engineering and Electronics, 2022, 44(1): 262-269.
[10]	Heliang YUAN, Tian JIN, Jiaqing QU, Hongli LYU. Processing technology of discontinuous satellite navigation signal under rotating condition [J]. Systems Engineering and Electronics, 2021, 43(9): 2573-2580.
[11]	Shuguang SUN, Qixin WEN. Aircraft height optimization algorithm of integrated navigation in terminal area based on height anomaly compensation [J]. Systems Engineering and Electronics, 2021, 43(9): 2612-2619.
[12]	Yuexin ZHAO, Wangdong QI, Peng LIU, En YUAN, Bing XU. Quadratic constraint Kalman filter algorithm for three dimensional AoA target tracking [J]. Systems Engineering and Electronics, 2021, 43(8): 2263-2272.
[13]	Chunhui LI, Jian MA, Yongjian YANG, Bingsong XIAO, Youwei DENG, Tao SHENG. Adaptive square-root cubature Kalman filter algorithm based on amending [J]. Systems Engineering and Electronics, 2021, 43(7): 1824-1830.
[14]	Shuang CONG, Kun ZHANG. Online quantum state estimation optimization algorithm based on Kalman filter [J]. Systems Engineering and Electronics, 2021, 43(6): 1636-1643.
[15]	Baojie CAI, Lei SHAO. Robust filtering algorithm based on three discriminant domain and least squares fitting [J]. Systems Engineering and Electronics, 2021, 43(5): 1346-1353.

Maximum correntropy Kalman filter used for hull deformation measurement in non-Gaussian environment

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 30

Related Articles 15

Recommended Articles

Metrics

Comments