微小型位置姿态测量系统(micro position and orientation system,MPOS)是为成像载荷提供实时高精度运动补偿信息的关键装置,其测量精度严重制约成像精度的提升。针对MPOS集中滤波实时性差的问题,在基于双ARM(advanced reduced instruct...微小型位置姿态测量系统(micro position and orientation system,MPOS)是为成像载荷提供实时高精度运动补偿信息的关键装置,其测量精度严重制约成像精度的提升。针对MPOS集中滤波实时性差的问题,在基于双ARM(advanced reduced instruction set computing machines)硬件平台的基础上,采用联邦滤波实时组合算法对多组传感器数据进行最优信息融合。以微惯性测量单元为公共参考系统,双天线全球导航卫星系统(global navigation satellite system,GNSS)和磁强计作为子系统提供量测信息,将各个子系统得到的局部误差协方差及状态估计值在主滤波器中进行信息融合得到最优估计值。通过动态实验验证了基于联邦滤波实时组合方法的MPOS,其位置、速度、航向角及水平姿态角精度可达0.6 m,0.03 m/s,0.15°,0.04°。展开更多
The concept of resilient positioning, navigation and timing (PNT) is described. The definition of resilient PNT is given, the relationship between integrated (or comprehensive) PNT and resilient PNT is analyzed, and i...The concept of resilient positioning, navigation and timing (PNT) is described. The definition of resilient PNT is given, the relationship between integrated (or comprehensive) PNT and resilient PNT is analyzed, and it is pointed out that the integrated PNT is the foundation of resilient PNT. Resilient PNT should be divided into resilient sensor integration, resilient functional model and resilient stochastic model. The strategy and principles of resilient integration of sensors are discussed. The resilient integration of sensors should be designed following the optimal, available, compatible and interoperable principles. The concepts of resilient functional model and possible modification strategies of the different functional models are also described. Several possible optimal routes for resilient stochastic model improvements are also set forth. It is pointed out that the optimal improvements of stochastic models for multi PNT sources should follow the same variance scale. At last, the resilient PNT data fusion for state parameters are given based on the resilient functional and stochastic models.展开更多
文摘微小型位置姿态测量系统(micro position and orientation system,MPOS)是为成像载荷提供实时高精度运动补偿信息的关键装置,其测量精度严重制约成像精度的提升。针对MPOS集中滤波实时性差的问题,在基于双ARM(advanced reduced instruction set computing machines)硬件平台的基础上,采用联邦滤波实时组合算法对多组传感器数据进行最优信息融合。以微惯性测量单元为公共参考系统,双天线全球导航卫星系统(global navigation satellite system,GNSS)和磁强计作为子系统提供量测信息,将各个子系统得到的局部误差协方差及状态估计值在主滤波器中进行信息融合得到最优估计值。通过动态实验验证了基于联邦滤波实时组合方法的MPOS,其位置、速度、航向角及水平姿态角精度可达0.6 m,0.03 m/s,0.15°,0.04°。
文摘The concept of resilient positioning, navigation and timing (PNT) is described. The definition of resilient PNT is given, the relationship between integrated (or comprehensive) PNT and resilient PNT is analyzed, and it is pointed out that the integrated PNT is the foundation of resilient PNT. Resilient PNT should be divided into resilient sensor integration, resilient functional model and resilient stochastic model. The strategy and principles of resilient integration of sensors are discussed. The resilient integration of sensors should be designed following the optimal, available, compatible and interoperable principles. The concepts of resilient functional model and possible modification strategies of the different functional models are also described. Several possible optimal routes for resilient stochastic model improvements are also set forth. It is pointed out that the optimal improvements of stochastic models for multi PNT sources should follow the same variance scale. At last, the resilient PNT data fusion for state parameters are given based on the resilient functional and stochastic models.
