置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用

[目的]为解决载体受到扰动时组合导航精度下降的问题,提出一种基于置信检验自适应联邦卡尔曼滤波(CC-AFKF)框架.[方法]首先,将电子罗盘(EC)、全球定位系统(GPS)与惯性导航系统(INS)相结合;其次,构建置信检验模型,有效滤除INS/GPS和INS/EC子系统中低置信度的量测值,保证量测值的准确性;最后,提出...

Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation

[Objectives]In order to solve the problem of the reduced accuracy of integrated navigation when a carrier is disturbed,a confidence check-adaptive federated Kalman filter(CC-AFKF)framework is proposed.[Methods]First,the electronic compass(EC),global positioning system(GPS)and inertial navigation system(INS)are combined.Second,a confidence check model is constructed to effectively filter out low-confidence measurements in the INS/GPS and INS/EC subsystems,and ensure the accuracy of the measured value.Finally,an adaptive adjustment factor strategy for the INS/GPS and INS/EC systems is proposed to effectively adjust system noise covariance during the update process.[Results]A large number of related tests are carried out through an underwater vehicle equipped with INS/GPS/EC integrated navigation systems.The test results show that the CC-AFKF algorithm proposed in this paper can improve the integrated accuracy of position and velocity by at least 29%compared with typical KF and FKF algorithms.[Conclusions]The results of this study can provide corresponding directions and ideas for research on loosely coupled integrated navigation systems.