Numerical Simulation of Magnetic Interference Parameter Identification of AUV Based on L-SHADE Agorithm

doi:10.12382/bgxb.2023.0599

Abstract

Abstract:

The autonomous underwater vehicle (AUV) magnetic measurement platform can be used for marine geomagnetic field measurement, underwater magnetic target detection and identification, etc. The AUV magnetic measurement platform has broad application prospects. However, at present, the magnetic interference compensation technology of AUV carrier is not mature, which restricts the magnetic measurement accuracy of underwater vehicle. Based on the basic principle of anti-magnetic interference of magnetic measurement platform, a numerical simulation method based on success history-based adaptive differential evolution with linear population size reduction (L-SHADE) algorithm is proposed for the identification of magnetization interference parameters of AUV carrier. A hybrid model of magnetic dipole and ellipsoid of rotation is used to equivalently simulate the magnetic interference of AUV carrier. Multiple groups of magnetic measurement data are obtained through simulated navigation, a magnetic interference parameter identification model is established accordingly, and L-SHADE algorithm is used to solve the problem. The propagation law of the magnetic measuring accuracy of magnetic measurement platform with the errors of magnetic sensor, platform attitude and heading is studied through quantitative analysis of numerical simulation experiments. When the measuring accuracy of magnetic sensor is 10nT, the attitude measuring accuracy is 0.01°, and the course measuring accuracy is 0.1°, the measuring error can be less than 100nT. The anti-jamming test of the designed AUV magnetic measurement platform shows that the maximum relative error of the total geomagnetic field is 1.07%.

Key words: autonomous underwater vehicle, magnetic interference compensation, parameter identification, magnetic equivalent model, success history-based adaptive differential evolution with linear population size reduction algorithm

CLC Number:

TM153⁺.1

ZHOU Guohua, LI Linfeng, WU Kena, LIU Yuelin, XIA Shuai. Numerical Simulation of Magnetic Interference Parameter Identification of AUV Based on L-SHADE Agorithm[J]. Acta Armamentarii, 2024, 45(8): 2678-2687.

Figures/Tables 15

Fig.1 Schematic diagram of mathematical model

Fig.2 Schematic diagram of attitude transformation

Fig.3 Schematic diagram of hybrid model

Fig.4 Mechanism scheme of linear parameter declination

Fig.5 Schematic diagram of model

Table 1 Parameter identification comparison sheet

辨识方法	K	m_p
理论计算	$- 0.78840 0 0.21950 0 - 0.92380 0 0.04190 0 1.07430$	$500 - 800 600$
模拟仿真	$- 0.78841 0 0.21948 0 - 0.92381 0 0.04190 0 1.07433$	$500.00004 - 800.00000 599.99999$

Table 1 Parameter identification comparison sheet

辨识方法	K	m_p
理论计算	$- 0.78840 0 0.21950 0 - 0.92380 0 0.04190 0 1.07430$	$500 - 800 600$
模拟仿真	$- 0.78841 0 0.21948 0 - 0.92381 0 0.04190 0 1.07433$	$500.00004 - 800.00000 599.99999$

Fig.6 Analysis of influence of magnetic sensor measuring accuracy

Fig.7 Analysis of influence of attitude measurement accuracy

Fig.8 Analysis of influence of heading measurement accuracy

Table 2 13 groups of typical sensor accuracy solutions

序号	磁传感器测量精度/nT	姿态测量精度/(°)	航向测量精度/(°)
1	100	0.1	1
2	1	0.001	1
3	100	0.1	0.5
4	10	0.1	0.5
5	1	0.1	0.5
6	10	0.05	0.5
7	10	0.1	0.1
8	10	0.05	0.1
9	1	0.05	0.1
10	10	0.01	0.1
11	1	0.01	0.1
12	0.1	0.005	0.05
13	0.01	0.001	0.01

Fig.9 Effect analysis of sensors with different precision

Fig.10 Layout of the experimental site

Fig.11 Convergence curve of magnetic interference parameters

Table 3 AUV magnetic field measurement data sheet

序号	B_p_x/ nT	B_p_y/ nT	B_p_z/ nT	航向角 φ/rad	横摇角 γ/rad	纵倾角 θ/rad
1	33253	8000	35755	0	0.26062	0.03970
2	33314	-1608	36591	0	0.00181	0.04947
3	33237	-11381	34913	0	-0.26326	0.05988
4	-1335	-24701	42688	1.5708	0.26191	0.04052
5	-1535	-34938	34928	1.5708	-0.0007	0.05033
6	-1770	-42911	24653	1.5708	-0.26270	0.06079
7	-34999	8045	33542	3.14159	0.26182	0.04119
8	-34957	-827	34681	3.14159	-0.00051	0.05000
9	-34999	-9717	33434	3.14159	-0.26287	0.06148
10	-143	41096	26644	4.71239	0.26088	0.04040
11	120	32745	36472	4.71239	-0.00120	0.04985
12	264	22196	43788	4.71239	-0.26117	0.06044

Table 4 Standard deviation sheet

算法	ΔB_x/nT	ΔB_y/nT	ΔB_z/nT	ΔB/nT
OLS	11.8	9.3	6.1	9.3
L-SHADE	6.0	12.7	5.8	7.8

References 32

[1]	郭成豹, 胡松, 王文井, 等. 利用磁传感器阵列磁场差值的舰船磁场反演建模方法[J]. 兵工学报, 2022, 43(1): 111-119. doi: 10.3969/j.issn.1000-1093.2022.01.012
	GUO C B, HU S, WANG W J, et al. Ship magnetic field inversion modeling method utilizing the magnetic field difference between magnetic sensors[J]. Acta Armamentarii, 2022, 43(1): 111-119. (in Chinese) doi: 10.3969/j.issn.1000-1093.2022.01.012
[2]	王锴松, 周国华, 刘月林, 等. 地磁模拟法测量舰船感应磁场的数值模拟[J]. 兵工学报, 2022, 43(3): 617-625. doi: 10.12382/bgxb.2021.0110
	WANG K S, ZHOU G H, LIU Y L, et al. Numerical simulation of measuring ship's induced magnetic field by geomagnetic field simulation method[J]. Acta Armamentarii, 2022, 43(3): 617-625. (in Chinese) doi: 10.12382/bgxb.2021.0110
[3]	周耀忠, 张国友. 舰船磁场分析计算[M]. 北京: 国防工业出版社, 2004.
	ZHOU Y Z, ZHANG G Y. The analysis and calculation of ship magnetic field[M]. Beijing: National Defense Industry Press, 2004. (in Chinese)
[4]	黄琰, 李岩, 俞建成, 等. AUV智能化现状与发展趋势[J]. 机器人, 2020, 42(2): 215-231. doi: 10.13973/j.cnki.robot.190392
	HUANG Y, LI Y, YU J C, et al. State-of-the-art and development trends of AUV intelligence[J]. Robot, 2020, 42(2): 215-231. (in Chinese) doi: 10.13973/j.cnki.robot.190392
[5]	王童豪, 彭星光, 潘光, 等. 无人水下航行器的发展现状与关键技术[J]. 宇航总体技术, 2017, 1(4): 52-64.
	WANG T H, PENG X G, PAN G, et al. Development and key technologies of unmanned underwater vehicles[J]. Astronautical Systems Engineering Technology, 2017, 1(4): 52-64. (in Chinese)
[6]	AHMED A M E, DUAN W Y. Overview on the development of autonomous underwater vehicles (AUVs)[J]. Journal of Ship Mechanics, 2016, 20(6):768-787.
[7]	ALLEN G I, MATTHEWS R, WYNN M. Mitigation of platform generated magnetic noise impressed on a magnetic sensor mounted in an autonomous underwater vehicle[C]//Proceedings of the MTS/IEEE Oceans 2001. Honolulu, HI, US: IEEE, 2001: 63-71.
[8]	ALLEN G I, PURPURA J, OVERWAY D. Measurement of magnetic noise characteristics on select auvs with some potential mitigation techniques[C]//Proceedings of the Oceans’02 MTS/IEEE. Biloxi, MI, US: IEEE, 2002:978-984.
[9]	WYNN M, BONO J. Magnetic sensor operation onboard an auv: magnetic noise issues and a linear systems approach to mitigation[C]// Oceans’02 MTS/IEEE. Biloxi, MI, US: IEEE, 2002: 985-993.
[10]	BONO J T, OVERWAY D J, WYNN W M. Magnetic sensor operation onboard a UUV: magnetic noise investigation using a total-field gradiometer[C]//Proceedings of the Oceans 2003. San Diego, CA, US:IEEE, 2003: 2018-2022.
[11]	WIEGERT R F, PRICE B L. Magnetic sensor development for mine countermeasures using autonomous underwater vehicles[J]. Proceedings of SPIE, 2000, 4039: 93-103.
[12]	CLEM T, ALLEN G, BONO J, et al. Magnetic sensors for buried minehunting from small unmanned underwater vehicles[C]//Proceedings of the Oceans’04 MTS/IEEE Techno-Ocean’04. Kobe, Japan: IEEE, 2004: 902-910.
[13]	KUMAR S, SKVORETZ D C, MOELLER C R, et al. Magnetic gradiometer for underwater detection applications[J]. Proceedings of SPIE, 2006, 6201: 62011 H.
[14]	PEI Y H, YEO H G, KANG X Y, et al. Magnetic gradiometer on an AUV for buried object detection[C]//Proceedings of Oceans 2010 MTS/IEEE Seattle. Seattle, WA, US: IEEE, 2010: 1-8.
[15]	TILLEY D, DHANAK M, AN E, et al. Characterizing magnetic sensors and magnetic noise of AUVs[C]//Proceedings of 2012 Oceans. Hampton Roads, VA, US: IEEE, 2012: 1-5.
[16]	WALKER C R, STRINGFIELD J Q, WOLBRECHT E T, et al. Measurement of the magnetic signature of a moving surface vessel with multiple magnetometer-equipped AUVs[J]. Ocean Engineering, 2013, 64: 80-87.
[17]	GALLIMORE E, TERRILL E, PIETRUSZKA A, et al. Magnetic survey and autonomous target reacquisition with a scalar magnetometer on a small AUV[J]. Journal of Field Robotics, 2020, 37(7): 1246-1266.
[18]	ARMSTRONG B, PENTZER J, ODELL D, et al. Field measurement of surface ship magnetic signature using multiple AUVs[C]//Proceedings of Oceans 2009. Biloxi, MS, US: IEEE, 2009: 1-9.
[19]	PENTZER J, CROSBIE B, BEAN T, et al. Measurement of magnetic field using collaborative AUVs[C]//Proceedings of Oceans’10 IEEE SYDNEY. Sydney, Australia: IEEE, 2010: 1-7.
[20]	WALKER C, ARMSTRONG B, BEAN T, et al. Calibration and localization of auv-acquired magnetic survey data[C]//Proceedings of Oceans’11 MTS/IEEE KONA. Waikoloa, HI, US: IEEE, 2011: 1-6.
[21]	WALKER C, STRINGFIELD J, WOLBRECHT E, et al. Survey of the magnetic signature of a moving surface vessel by multiple AUVs[C]//Proceedings of 2012 Oceans. Hampton Roads, VA, US: IEEE, 2012: 1-7.
[22]	LV J W, YU Z T, HUANG J L, et al. The compensation method of vehicle magnetic interference for the magnetic gradiometer[J]. Advances in Mathematical Physics, 2013, 2013: 1-5.
[23]	LI J, ZHANG Q, CHEN D X, et al. Magnetic interferential field compensation in geomagnetic measurement[J]. Transactions of the Institute of Measurement and Control, 2014, 36(2): 244-251.
[24]	ZHANG Q, PANG H F, WAN C B. Magnetic interference compensation method for geomagnetic field vector measurement[J]. Measurement, 2016, 91: 628-633.
[25]	刘雪君. 基于水下机器人的磁干扰补偿算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
	LIU X J. The study of the magnetic interference compensation algorithm of underwater robot[D]. Harbin:Harbin Engineering University, 2018. (in Chinese)
[26]	WU T, TAO C H, ZHANG J H, et al. Correction of tri-axial magnetometer interference caused by an autonomous underwater vehicle near-bottom platform[J]. Ocean Engineering, 2018, 160: 68-77.
[27]	LI L F, ZHANG R, LIU W D, et al. Magnetic signature measurement of surface ship using a rov-equipped with magnetometer[C]//Proceedings of Global Oceans 2020:Singapore- U.S. Gulf Coast. Biloxi, MS, US: IEEE, 2020: 1-5.
[28]	曹军宏, 刘飞. 基于ROV的舰船磁场测量方法[J]. 水下无人系统学报, 2021, 29(6): 754-759.
	CAO J H, LIU F. Magnetic field measurement method based on remotely operated vehicle[J]. Journal of Unmanned Undersea Systems, 2021, 29(6): 754-759. (in Chinese)
[29]	陈进明, 隗燕琳. 基于神经网络的磁性目标磁矩反演方法[J]. 船电技术, 2012, 32(9): 57-60.
	CHEN J M, WEI Y L. Magnetic moments estimation Method of a magnetic object based on neural network[J]. Marine Electric & Electronic Technology, 2012, 32(9): 57-60. (in Chinese)
[30]	王志锋, 刘大明. 基于差分进化的共面矩形线圈均匀磁场优化[J]. 船电技术, 2012, 32(12): 29-31, 34.
	WANG Z F, LIU D M. Optimization of uniform field of rectangular coil by differential evolution[J]. Marine Electric & Electronic Technology, 2012, 32(12): 29-31, 34. (in Chinese)
[31]	卞强, 刘大明, 童余德, 等. 基于差分进化算法的铁磁物体磁化率优化方法[J]. 海军工程大学学报, 2015, 27(3): 1-4, 52.
	BIAN Q, LIU D M, TONG Y D, et al. Method for optimizing magnetic susceptibility of ferromagnetic objects based on DE algorithm[J]. Journal of Naval University of Engineering, 2015, 27(3): 1-4,52. (in Chinese)
[32]	TANABE R, FUKUNAGA A S. Improving the search performance of shade using linear population size reduction[C]//Proceedings of 2014 IEEE Congress on Evolutionary Computation. Beijing, China: IEEE, 2014: 1658-1665.