Applying GPS Time-Differenced Carrier Phase velocity estimation for Calibration of LDV/INS Integrated Navigation Systems in land vehicles

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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26th Saint Petersburg International Conference on Integrated Navigation Systems Proceedings

2019 26th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) ◽

10.23919/icins.2019.8769366 ◽

2019 ◽

Keyword(s):

Navigation Systems ◽

Integrated Navigation ◽

Saint Petersburg ◽

International Conference

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A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽

10.3390/s21092922 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2922

Author(s):

Fan Zhang ◽

Ye Wang ◽

Yanbin Gao

Keyword(s):

Fault Detection ◽

Parametric Bootstrap ◽

Integrated System ◽

Range Data ◽

Navigation Systems ◽

Integrated Navigation ◽

Multiple Faults ◽

Detection And Identification ◽

Fault Detection And Identification ◽

Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

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A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Navigation ◽

10.1002/navi.435 ◽

2021 ◽

Author(s):

Nader Al Bitar ◽

Alexander Gavrilov

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Navigation Systems ◽

Integrated Navigation ◽

Novel Approach

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MEMS Based SINS/OD Filter for Land Vehicles’ Applications

Mathematical Problems in Engineering ◽

10.1155/2017/1691320 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Huisheng Liu ◽

Zengcai Wang ◽

Susu Fang ◽

Chao Li

Keyword(s):

Navigation System ◽

Velocity Measurement ◽

Microelectromechanical Systems ◽

Low Cost ◽

Detection Algorithm ◽

Measurement Unit ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Land Vehicles ◽

Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.245.323 ◽

2012 ◽

Vol 245 ◽

pp. 323-329 ◽

Cited By ~ 4

Author(s):

Muhammad Ushaq ◽

Jian Cheng Fang

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Sensor ◽

Inertial Navigation ◽

Measurement Unit ◽

Navigation Systems ◽

Integrated Navigation ◽

Efficient Manner ◽

Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

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