Abstract
Unknown biases or perturbations in the INS/GNSS integrated navigation system may produce unforeseeable negative effects when the navigation states are estimated by using the Kalman filtering and its variants. To mitigate these undesirable effects in the INS/GNSS integrated navigation, a novel partially strong tracking extended consider Kalman filtering (PSTECKF) is proposed. In the presented PSTECKF algorithm, the biases are not estimated, but their covariance and co-covariance are incorporated into the state estimation covariance by using a nonlinear “consider” approach. Based on the above, the PSTECKF also partially introduces an adaptive fading factor into the predicted covariance of the states, which excludes the co-covariance between the states and biases, to compensate the nonlinear approximation errors and navigation system covariance uncertainties. Simulation results demonstrate the performance of the proposed PSTECKF for INS/GNSS integrated navigation is superior to that of the EKF and ECKF when the biases or perturbations happen in a navigation system.
Highlights
The inertial navigation system (INS) and global navigation satellite system (GNSS) integrated navigation system organically merge advantages of two sensors, which are the high short-term navigation accuracy of INS and high long-term navigation accuracy of GNSS, and have a widely application in navigation and positioning field [1]–[5]
When the states of the INS/GNSS integrated navigation system are estimated by using the Kalman filtering and its variants, there are two methods can be selected, which are the direct method and the indirect method [6]
This paper proposed a partially strong tracking extended consider Kalman filtering (PSTECKF) to overcome the navigation system model uncertainties and mitigate the negative effects of the biases in INS/GNSS integrated navigation system
Summary
The inertial navigation system (INS) and global navigation satellite system (GNSS) integrated navigation system organically merge advantages of two sensors, which are the high short-term navigation accuracy of INS and high long-term navigation accuracy of GNSS, and have a widely application in navigation and positioning field [1]–[5]. When the states of the INS/GNSS integrated navigation system are estimated by using the Kalman filtering and its variants, there are two methods can be selected, which are the direct method and the indirect method [6]. To utilize the direct method for the INS/GNSS integrated navigation system needs to solve some key technology problems, such as model nonlinearity, drifts of the inertial measurement unit (IMU), biases of the system and perturbations of the whole navigation system [6], [10]. This paper proposed a partially strong tracking extended consider Kalman filtering (PSTECKF) to overcome the navigation system model uncertainties and mitigate the negative effects of the biases in INS/GNSS integrated navigation system. The measurement model is constructed by using the output information of velocity and position from GNSS [7], [10], [30]
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