scholarly journals Nonlinear state estimation for inertial navigation systems with intermittent measurements

Automatica ◽  
2020 ◽  
Vol 122 ◽  
pp. 109244
Author(s):  
Miaomiao Wang ◽  
Abdelhamid Tayebi
2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.


2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.


Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.


2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.


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