{"title":"Calibration of three-axis strapdown magnetometers using Particle Swarm Optimization algorithm","authors":"Zhitian Wu, Yuanxin Wu, Xiaoping Hu, Mei-ping Wu","doi":"10.1109/ROSE.2011.6058522","DOIUrl":null,"url":null,"abstract":"In this work a new algorithm is developed for onboard calibration of three-axis strapdown magnetometers. The sensor errors, namely hard iron, soft iron, nonorthogonality, scale factors, biases and among others are taken into account. Particle Swarm Optimization (PSO) strategy is applied to estimate the errors above. The advantages of this method are no need for good initial values or linearization, easy realization and fast convergence. Accuracy and robustness of the proposed algorithm are validated by experiments. The post-calibration residuals are down to less than 10nT compared with approximate 1000nT before calibration. The proposed algorithm yields robust results with sufficient accuracy in tests.","PeriodicalId":361472,"journal":{"name":"2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROSE.2011.6058522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
In this work a new algorithm is developed for onboard calibration of three-axis strapdown magnetometers. The sensor errors, namely hard iron, soft iron, nonorthogonality, scale factors, biases and among others are taken into account. Particle Swarm Optimization (PSO) strategy is applied to estimate the errors above. The advantages of this method are no need for good initial values or linearization, easy realization and fast convergence. Accuracy and robustness of the proposed algorithm are validated by experiments. The post-calibration residuals are down to less than 10nT compared with approximate 1000nT before calibration. The proposed algorithm yields robust results with sufficient accuracy in tests.