An in-motion alignment method based on vehicle-carried RINS and GPS

Abstract

In order to realize the rapid start-up of vehicle-carried inertial navigation system, the paper studies the in-motion alignment method based on onboard rotating inertial navigation system (RINS) and GPS velocity information. According to the rotation characteristics of the rotating inertial navigation system, the Kalman filter model was established, including the installation angles between gyros, the lever arm and the asynchronous time, by measuring the velocity difference between RINS and GPS. Besides, multiple simulations and experimental verifications of the proposed in-motion alignment method have been carried out. The results show that the proposed in-motion alignment method can realize the rapid estimation of platform deflection angle, gyro installation errors, lever arm and asynchronous time of RINS in the process of motion, which verifies the effectiveness of the proposed in-motion alignment method.

Integrated navigation model based on TDCP constrained algorithm

“…The vehicle velocity information v T and its covariance R T output by the TDCP model are obtained by formula (8), and the vehicle velocity information v D and its covariance R D in the dynamic process of the DGNSS velocity measurement model are obtained by formula (10). Because the two kinds of vehicle velocity information v T and v D both obey the normal distribution of the true value of the carrier velocity, then v T − v D obeys the normal distribution with a mean value of 0 and a covariance matrix of R T + R D , and then the vehicle velocity test statistics can be constructed:…”

“…In dynamic alignment, the Kalman filter model is mainly established by using the velocity difference of the two systems to realize fast estimation of attitude matrix and gyroscope deviation, and achieve faster alignment velocity and higher alignment accuracy [7,8]. On the other hand, the initial heading alignment can be achieved by matching the absolute velocity trajectory with the relative trajectory obtained by INS dead reckoning [9].…”

An improved GNSS/INS navigation method based on cubature Kalman filter for occluded environment

“…If we consider it to be the result of a dynamic process rather than a static test, it can be expressed as an increase in the system noise. However, because the IMU often undergoes strict calibration before leaving the factor and with the help of mature calibration methods, system noise usually does not change significantly [28,29]. In other words, the system noise can be seen as stable.…”

“…After being initialized by CKF, the algorithm will first be updated according to the time update process and the first half of the measurement update process by ( 22) and ( 5)- (8). Then the innovation vector is obtained by (29). When entering the part of the adaptive factor, MD is first calculated using (30).…”

Anti-disturbance self-alignment method for dual-axis rotational inertial navigation system under marine mooring conditions

“…By using a rotation mechanism to periodically rotate the inertial measurement unit (IMU), the observability of the system can be improved and the constant error of the inertial sensor can be eliminated, thereby improving the accuracy and rapidity of the initial alignment * Author to whom any correspondence should be addressed. [6][7][8]. Compared with the single-axis rotational INS (RINS) that cannot modulate the constant error of the vertical inertial sensor, the dual-axis RINS provides the ability to independently rotate the IMU around two axes, so that the inertial sensor error can be modulated in three directions.…”