Wei Gao

Bio: Wei Gao is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Inertial navigation system & Kalman filter. The author has an hindex of 9, co-authored 33 publications receiving 295 citations.

Rapid Fine Strapdown INS Alignment Method under Marine Mooring Condition

Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

Generalized Method for Improved Coning Algorithms using Angular Rate

Abstract: Conventional attitude algorithms for strapdown inertial navigation system (SINS) use angular increment as algorithm input. However, these algorithms do not address the issue of angular rate which is the output of modern-day gyro. The integration of angular rate for the purpose of getting angular increment degrades the algorithm performance inevitably. To solve this problem, a generalized method for improved coning algorithms is proposed. The improved coning algorithms use angular rate as algorithm input and estimate the coning term with the cross product of angular rates. Simulations are carried out under coning motion. While angular rate is used as algorithm input, the accuracy of attitude computation can be improved effectively by using improved coning algorithms as compared with conventional ones.

A new method of initial alignment and self-calibration based on dual-axis rotating strapdown inertial navigation system

Abstract: Initial alignment is one of the key technologies of the inertial navigation. The initial alignment precision affects the navigation result. Researches show that the result of initial alignment depends on the observability of the system state variables. So it's necessary to enhance the system states observability to improve the accuracy and speed. A positioning method based on dual-axis rotating strapdown initial navigation system (SINS) is introduced in this paper which enhances the system states observability and improves the observability degree by changing the system error model matrix periodically. The PWCS (Piece-Wise Constant System) and SVD (Singular Value Decomposition) are used to analyse the observability. The results show that the system observability is improved; the misalignment angles and the gyroscope constant drifts are estimated in 1 hour, and accelerometers bias are estimated in 0.8 hour with the new dual-axis rotating inertial alignment and self-calibration method.

Analysis of error for a rotating strap-down inertial navigation system with fibro gyro

Abstract: The error equation of a rotating inertial navigation system was introduced. The effect of the system’s main error source (constant drift of gyro and zero bias of accelerometer) under rotating conditions for the system was analyzed. Validity of theoretical analysis was shown via simulation, and that provides a theoretical foundation for a rotating strap-down inertial navigation system during actual experimentation and application.

Improved filter estimation method applied in zero velocity update for SINS

Abstract: Zero Velocity Update (ZUPT) utilizes the zero velocity condition for stationary Strapdown Inertial Navigation System (SINS), executes navigation errors estimation and emendation to control SINS position growth. The improved filter estimation ZUPT is proposed in this paper. Two technologies, separate-bias kalman filter and yaw error rapid estimation, are applied in the proposed method. Separate-bias kalman filter solve the divergence problem for setting a mass of kalman filter state variances. And the yaw error rapid estimation can calculate the unobservable yaw error in periodic ZUPT. Simulation results show that the estimations of attitude errors, yaw error and position errors converge to the right values quickly in the proposed ZUPT, and the position accuracy is improved effectively compared to the conventional one.

A Self-Calibration Method for Nonorthogonal Angles Between Gimbals of Rotational Inertial Navigation System

Abstract: Navigation accuracy of an inertial navigation system can be significantly enhanced by rotating inertial measurement unit with gimbals. Therefore, nonorthogonal angles of gimbals, which are coupled into the navigation error during rotation, should be calibrated and compensated effectively. In this paper, the relationship model of nonorthogonal angles and navigation error is established. Then, the calibration scheme and observation equation during gimbals rotation is proposed. Proved by a piecewise constant system method, all of the error parameters are observable and can be estimated by an extended Kalman filter. Experimental results show that compared with the traditional method, the proposed method can substantially reduce velocity error on static base. Moreover, the position accuracy of long-term navigation under moving base is also significantly increased.

Global Positioning Systems

Abstract: Introduction: Global Positioning Systems (GPS) offer many benefits in assisting school transportation operations achieve their mission. A variety of systems exist with varying capabilities. These systems offer not only positioning information in nearly real time and/or delayed time, but also vehicle position history, travel route, with speed and time, student identification, emergency alert, and automated accident notification. Recognizing that many systems are available that provide this information, and further, that this information is valuable in efficiently managing the school bus fleet, and further, that standardization of minimum information across the State of Michigan would be beneficial to school districts, the Pupil Transportation Advisory Committee recommends the Michigan Department of Education adopt the following as a recommended guideline for purchasing GPS equipment. ________________________________________________________________

A Fast SINS Initial Alignment Scheme for Underwater Vehicle Applications

Abstract: To achieve high Strapdown Inertial Navigation System (SINS) alignment accuracy within a short period of time is still a challenging issue for underwater vehicles. In this paper, a new SINS initial alignment scheme aided by the velocity derived from Doppler Velocity Log (DVL) is proposed to solve this problem. In the stage of the coarse alignment, the velocity of DVL is employed to reduce the impact of the linear motion. With a backtracking framework, the fine alignment runs with the data recorded during the process of the coarse alignment and thus will speed up the overall alignment process. In addition, by using this new scheme, it is equivalent to length the alignment time for both coarse and fine alignments, so the accuracy of the alignments will be improved. In order to reduce the volume of the data that has to be recorded, a new model for SINS fine alignment is derived in the inertial reference frame which makes it feasible for real time applications. The experimental results are presented for both unaided static and in-motion alignment using DVL aiding. It is clearly shown that the proposed method meets the requirement of SINS alignment for underwater vehicles.

A novel scheme for DVL-aided SINS in-motion alignment using UKF techniques

Abstract: In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.