一种基于因子图的异步信息融合定位算法
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摘要
为了解决惯导/里程计/地磁匹配组合定位系统非等间隔数据融合带来拟合误差、信息源无法即插即用的问题,提出了一种基于因子图的异步多源组合定位算法。采用因子图关联了系统状态模型与观测模型,基于最大后验概率进行状态估计,充分利用了GMNS的非等间隔定位信息,消除了外推法的拟合误差。对组合定位算法进行了仿真,仿真结果表明:该算法的水平与高程定位误差分别减少到采用GMNS信息直接校正惯导位置方法的三分之一;进行了跑车试验,试验结果验证:该算法具备即插即用功能,提高了组合定位系统的可靠性。
In order to solve the fitting error brought by the non-interval information fusion of the Inertial Navigation System(INS)/Odometer(OD)/Geomagnetic Matching Navigation System(GMNS) integrated positioning system and the information source cannot plug-and-play, an asynchronous multi-source information fusion positioning algorithm based on factor graph is proposed.The state model and observation model are linked by factor graph and the state estimation is based on the maximum posteriori probability. The non-interval positioning information of GMNS is fully utilized and the fitting error of the extrapolation method is eliminated. A simulation test is conducted whose results are presented that the level and elevation error of the algorithm are reduced to the third of the direct correction of the position of inertial navigation using GMNS information. An experiment is conducted whose results are presented that the algorithm has plug-and-play capability which improves the reliability of the integrated positioning system.
In order to solve the fitting error brought by the non-interval information fusion of the Inertial Navigation System(INS)/Odometer(OD)/Geomagnetic Matching Navigation System(GMNS) integrated positioning system and the information source cannot plug-and-play, an asynchronous multi-source information fusion positioning algorithm based on factor graph is proposed.The state model and observation model are linked by factor graph and the state estimation is based on the maximum posteriori probability. The non-interval positioning information of GMNS is fully utilized and the fitting error of the extrapolation method is eliminated. A simulation test is conducted whose results are presented that the level and elevation error of the algorithm are reduced to the third of the direct correction of the position of inertial navigation using GMNS information. An experiment is conducted whose results are presented that the algorithm has plug-and-play capability which improves the reliability of the integrated positioning system.
引文
[1]赵万龙,孟维晓,韩帅.多源融合导航技术综述[J].遥测遥控,2016(6):54-60.Zhao Wanlong,Meng Weixiao,Han Shuai.A survey of multi-source information fusion navigation[J].Journal of Telemetry,Tracking and Command,2016(6):54-60.
[2]周锐,等.多传感器融合目标跟踪[J].航空学报,1998,19(5):536-540.Zhou Rui,et al.Target tracking based on fusion of multi-sensor[J].Acta Aeronautica et Astronautica Sinica,1998,19(5):536-540.
[3]王宝树,李芳社.基于数据融合技术的多目标跟踪算法研究[J].西安电子科技大学学报,1998,25(3):269-272.Wang BaoShu,Li Fangshe.Algorithm for multi-sensor data fusion target tracking[J].Journal of xidian University,1998,25(3):269-272.
[4]李莉.时间配准在多传感器数据处理中的应用[J].仪器仪表用户,2011,18(3):65-66.Li Li.Registration and more time processing of sensor data[J]Instrumen-tation Customer,2011,18(3):65-66.
[5]黄显林,卢鸿谦,王宇飞.组合导航非等间隔联合滤波[J].中国惯性技术学报,2002,10(3):1-7.Huang Xianlin,Lu Hongqian,Wang Yufei.Incoordinate interval federated filtering of integrated navigation[J].Journal of Chinese Inertial Technology,2002,10(3):1-7.
[6]Chiu H P,et al.Constrained optimal selection for multi-sensor robot navigation using plug-and-play factor graphs[J].IEEE International Conference,2014:663-670.
[7]王桂凤.基于因子图的信道均衡研究[D].大连:大连理工大学,2011.Wang Guifeng.Research on channel equalization based on factor graph[D].Dalian:Dalian University of Technology,2011.
[8]Kschischang F R,et al.Factor graphs and the sum-product algorithm[J].IEEE Transactions on Information Theory,2001,47(2):498-519.
[9]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理(第3版)[M].西安:西北工业大学出版社,2015.Qin Yongyuan,Zhang Hongyan,Wang Shuhua.Kalman filter and integrated navigation principle(3rd edition)[M].Xi'an:Northwest Polytechnic University Press,2015.
[10]张天光,王秀萍,王丽霞.捷联惯性导航技术(第二版)[M].北京:国防工业出版社,2007.Zhang Tianguang,Wang Xiuping,Wang Lixia.Strapdown inertial navigation technology(2nd edition)[M].Beijing:National Defense Industry Press,2007.
[2]周锐,等.多传感器融合目标跟踪[J].航空学报,1998,19(5):536-540.Zhou Rui,et al.Target tracking based on fusion of multi-sensor[J].Acta Aeronautica et Astronautica Sinica,1998,19(5):536-540.
[3]王宝树,李芳社.基于数据融合技术的多目标跟踪算法研究[J].西安电子科技大学学报,1998,25(3):269-272.Wang BaoShu,Li Fangshe.Algorithm for multi-sensor data fusion target tracking[J].Journal of xidian University,1998,25(3):269-272.
[4]李莉.时间配准在多传感器数据处理中的应用[J].仪器仪表用户,2011,18(3):65-66.Li Li.Registration and more time processing of sensor data[J]Instrumen-tation Customer,2011,18(3):65-66.
[5]黄显林,卢鸿谦,王宇飞.组合导航非等间隔联合滤波[J].中国惯性技术学报,2002,10(3):1-7.Huang Xianlin,Lu Hongqian,Wang Yufei.Incoordinate interval federated filtering of integrated navigation[J].Journal of Chinese Inertial Technology,2002,10(3):1-7.
[6]Chiu H P,et al.Constrained optimal selection for multi-sensor robot navigation using plug-and-play factor graphs[J].IEEE International Conference,2014:663-670.
[7]王桂凤.基于因子图的信道均衡研究[D].大连:大连理工大学,2011.Wang Guifeng.Research on channel equalization based on factor graph[D].Dalian:Dalian University of Technology,2011.
[8]Kschischang F R,et al.Factor graphs and the sum-product algorithm[J].IEEE Transactions on Information Theory,2001,47(2):498-519.
[9]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理(第3版)[M].西安:西北工业大学出版社,2015.Qin Yongyuan,Zhang Hongyan,Wang Shuhua.Kalman filter and integrated navigation principle(3rd edition)[M].Xi'an:Northwest Polytechnic University Press,2015.
[10]张天光,王秀萍,王丽霞.捷联惯性导航技术(第二版)[M].北京:国防工业出版社,2007.Zhang Tianguang,Wang Xiuping,Wang Lixia.Strapdown inertial navigation technology(2nd edition)[M].Beijing:National Defense Industry Press,2007.