In-Field Calibration of Triaxial Accelerometer Based on Beetle Swarm Antenna Search Algorithm

被引:15
作者
Wang, Pengfei [1 ]
Gao, Yanbin [1 ]
Wu, Menghao [1 ]
Zhang, Fan [1 ]
Li, Guangchun [1 ]
机构
[1] Harbin Engn Univ, Collage Automat, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
calibration; inertial measurement unit; triaxial accelerometer; intelligent optimization algorithm; beetle swarm antenna search algorithm; NAVIGATION; GYROS;
D O I
10.3390/s20030947
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Traditional calibration method is usually performed with expensive equipments such as three-axis turntable in a laboratory environment. However in practice, in order to ensure the accuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibrate the inertial measurement unit (IMU) without external equipment in the field. In this paper, a new in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search (BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithm and its improvements based on basic beetle antennae search (BAS) algorithm are introduced in detail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established for higher calibration accuracy, and then 24 optimal measurement positions are designed by theoretical analysis. In addition, the calibration procedures are improved according to the characteristics of BSAS algorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm. Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, which shows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate that the proposed method can achieve high precision in-field calibration without any external equipment, and meet the accuracy requirements of the INS.
引用
收藏
页数:21
相关论文
共 38 条
[11]   Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System [J].
Fan, Yuqi ;
Shao, Junpeng ;
Sun, Guitao .
SENSORS, 2019, 19 (12)
[12]   Prediction of dissolved gases content in power transformer oil using BASA-based mixed kernel RVR model [J].
Fei, Sheng-Wei ;
He, Chuang-Xin .
INTERNATIONAL JOURNAL OF GREEN ENERGY, 2019, 16 (08) :652-656
[13]  
Ferraris F., 1995, Sensors and Materials, V7, P311
[14]   MEMS gyros temperature calibration through artificial neural networks [J].
Fontanella, Rita ;
Accardo, Domenico ;
Lo Moriello, Rosario Schiano ;
Angrisani, Leopoldo ;
De Simone, Domenico .
SENSORS AND ACTUATORS A-PHYSICAL, 2018, 279 :553-565
[15]   Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging [J].
Gross, Jason N. ;
Gu, Yu ;
Rhudy, Matthew B. .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2015, 12 (03) :935-944
[16]   Metaheuristic research: a comprehensive survey [J].
Hussain, Kashif ;
Salleh, Mohd Najib Mohd ;
Cheng, Shi ;
Shi, Yuhui .
ARTIFICIAL INTELLIGENCE REVIEW, 2019, 52 (04) :2191-2233
[17]  
Jiang X., 2017, International Journal of Robotics and Control, V1, P1, DOI [10.5430/ijrc.v1n1p1, DOI 10.5430/IJRC.V1N1P1]
[18]  
Lin XM, 2018, CHIN AUTOM CONGR, P3701, DOI 10.1109/CAC.2018.8623171
[19]   An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots [J].
Liu, Jianfeng ;
Pu, Jiexin ;
Sun, Lifan ;
He, Zishu .
SENSORS, 2019, 19 (04)
[20]   A Comprehensive Overview of Inertial Sensor Calibration Techniques [J].
Poddar, Shashi ;
Kumar, Vipan ;
Kumar, Amod .
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2017, 139 (01)