第45卷第1期2024年1月仪器仪表学报ChineseJournalofScientificInstrumentVol.45No.1Jan.2024DOI:10.19650/j.cnki.cjsi.J2312045收稿日期:2023-10-20ReceivedDate:2023-10-20∗基金项目:国家自然科学基金(61873064)、江苏省重点研发计划(BE2022139)项目资助基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法∗陈熙源,周云川,钟雨露,戈明明(东南大学仪器科学与工程学院南京210096)摘要:复杂环境下的量测粗差和时变噪声严重影响了状态估计的精度和可靠性,对此提出了一种基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法。首先,基于先验和后验两阶段更新将变分贝叶斯推断引入因子图优化框架中,以估计时变量测噪声协方差;其次,利用相邻帧间的平均新息构造量测协方差预测值,作为粗差判据来实现稳健估计。基于INS/GNSS组合导航的仿真和现场实验评估表明,所提方法能在粗差干扰的情况下有效估计时变量测噪声,相比M估计和滑动窗口自适应因子图优化算法的水平定位误差分别减小了26.7%和39.8%,兼顾了估计精度和抗差性能,具有较好的复杂环境适应性。关键词:因子图优化;变分贝叶斯;组合导航;鲁棒自适应估计中图分类号:TH89TN96文献标识码:A国家标准学科分类代码:460.40510.40RobustadaptivefactorgraphoptimizationintegratednavigationalgorithmbasedonvariationalBayesianChenXiyuan,ZhouYunchuan,ZhongYulu,GeMingming(SchoolofInstrumentScienceandEngineering,SoutheastUniversity,Nanjing210096,China)Abstract:Theaccuracyandreliabilityofstateestimationareseriouslyaffectedbymeasurementoutliersandtime-varyingnoiseincomplexenvironments.Toaddresstheseissues,arobustadaptivefactorgraphoptimization(FGO)integratednavigationalgorithmbasedonvariationalBayesianisproposed.First,thevariationalBayesianinferenceisintroducedintotheFGOframeworkbasedonaprioriandaposterioritwo-stageupdatingtoestimatethetime-varyingmeasurementnoisecovariance.Secondly,themeaninnovationbetweenneighboringkeyframesisusedtoconstructmeasurementcovariancepredictionasanoutlierjudgmenttoachieverobustestimation.SimulationandfieldtestsbasedonINS/GNSSintegratednavigationshowthattheproposedmethodcaneffectivelyestimatethetime-varyingmeasurementnoisecovarianceinthepresenceofoutlierinterference,andreducethehorizontalpositionerrorb...
