Abstract
The current research on integrated navigation is mainly focused on filtering or integrated navigation equipment. Studies systematically comparing and analyzing how to choose appropriate integrated filtering methods under different circumstances are lacking. This paper focuses on integrated navigation filters that are used by different filters and attitude parameters for inertial integrated navigation. We researched integrated navigation filters, established algorithms, and examined the relative merits for practical integrated navigation. Some suggestions for the use of filtering algorithms are provided. We completed simulations and car-mounted experiments for low-cost strapdown inertial navigation system (SINS) to assess the performance of the integrated navigation filtering algorithms.
Highlights
An integrated navigation system is a system using two or more nonsimilar navigation systems to measure navigation information and calculate or correct the errors of the navigation system from measurements [1,2,3,4]
To fill the literature gap about the characteristics and applicable settings of low-precision inertial navigation integrated navigation, this paper focuses on those filters, especially in terms of algorithm establishment, characteristics, the merits of the specific integrated navigation system, and the applicable environment
For the microelectromechanical systems (MEMS)/global positioning system (GPS) car-mounted experiment, we chose attitude, velocity, position, and gyro bias for comparison. In this car-mounted experiment, we focused on the estimated performance of attitude and gyro bias with the velocity and position measurements obtained from the GPS
Summary
An integrated navigation system is a system using two or more nonsimilar navigation systems to measure navigation information and calculate or correct the errors of the navigation system from measurements [1,2,3,4]. In 1995, Julier and Uhlmann proposed the unscented Kalman filter (UKF) algorithm, which does not ignore the high-order terms of Taylor expansion by unscented transform (UT), and has high estimate accuracy in nonlinear systems [22]. We can apply linear or nonlinear Kalman filters in inertial integrated navigation. The earliest integrated navigation filter was constructed from the Euler angle and inertial error equations, which is called Euler-KF in this paper. As the attitude transformation matrix uses quaternion, the filters need to use nonlinear Kalman filters such as EKF and UKF. Inertial integrated navigation based on EKF using multiplicative quaternion is called the multiplicative EKF (MEKF) algorithm. We focus on Euler-KF, MEKF, USQUE, MRP-UKF, and DoEuler-UKF These five filters are currently common or research hotspots in integrated filtering methods.
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