scholarly journals Vehicles Positioning with the Fusion of Time of Arrival, Angle of Arrival and Inertial Measurements in the Extended Kalman Filter

2021 ◽  
Vol 7 (2) ◽  
pp. 51-67
Author(s):  
G. Fokin ◽  
A. Vladyko
Keyword(s):  
Kalman Filter ◽  
Coordinate System ◽  
Extended Kalman Filter ◽  
Inertial Navigation ◽  
Time Of Arrival ◽  
Navigation Systems ◽  
Angle Of Arrival ◽  
Arrival Angle ◽  
Radio Measurements ◽  
Inertial Measurements

This work is devoted to the study of models and methods for improving posi-tioning accuracy in ultra-dense V2X/5G radio access networks for vehicles during maneuvers by combining range and angle primary measurements with measurements of inertial navigation systems in the extended Kalman filter. Onboard platformless inertial navigation system is represented by three-axis accelerometer and gyroscope modules. Integration of primary inertial measurements of acceleration and angular velocity with primary radio measurements of range and angle is carried out by converting the inertial coordinate system of the accelerometer and gyroscope into coordinate system of vehicle using quaternions. Secondary processing of inertial and radio measurements is carried out in the extended Kalman filter. The integration results show an increase in the accuracy of estimating the trajectory of a vehicle from several meters to one meter when turning at an inter-section.

scholarly journals Improving the speed of initial alignment for marine strapdown inertial navigation systems using heading control signal feedback in extended Kalman filter

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989484 ◽  
Author(s):  
Hossein Rahimi ◽  
Amir Ali Nikkhah
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation ◽  
Control Signal ◽  
Navigation Systems ◽  
Alignment Method ◽  
Initial Alignment ◽  
Inertial Navigation Systems ◽  
Strapdown Inertial Navigation

In this article, a method was proposed for strapdown inertial navigation systems initial alignment by drawing on the conventional alignment method for stable platform navigation systems. When a vessel is moored, the strapdown inertial navigation system contributes to the disturbing motion. Moreover, the conventional methods of accurate alignment fail to succeed within an acceptable period of time due to the slow convergence of the heading channel in the mooring conditions. In this work, the heading was adjusted using the velocity bias resulting from the component of the angular velocity of the Earth on the east channel on the strapdown inertial navigation systems analytic platform plane to accelerate convergence in the initial alignment of navigation system. To this end, an extended Kalman filter with control signal feedback was used. The heading error was calculated using the north channel residual velocity of the strapdown inertial navigation systems analytic platform plane and was entered into an extended Kalman filter. Simulation and turntable experimental tests were indicative of the ability of the proposed alignment method to increase heading converge speed in mooring conditions.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

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.

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

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

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
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.

Studying the Сonsistency of Extended Kalman Filter in Pedestrian Navigation with Foot-Mounted SINS

2021 ◽  
Vol 29 (2) ◽  
pp. 59-77
Author(s):  
Yu.V. Bolotin ◽  
◽  
A.V. Bragin ◽  
D.V. Gulevskii ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Stance Phase ◽  
Navigation Systems ◽  
Velocity Data ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Condition ◽  
Dynamic Errors

The paper focuses on pedestrian navigation with foot-mounted strapdown inertial navigation systems (SINS). Zero velocity updates (ZUPT) during the stance phase are commonly applied in such systems to improve the accuracy. Zero velocity data are processed by the extended Kalman filter (EKF). Zero velocity condition is written in two forms: in reference and body frames. The first form traditional for pedestrian navigation is shown to provide an inconsistent EKF. The second form provides a correct ZUPT algorithm, which is naturally written in so-called dynamic errors. The analyzed algorithm for data fusion from two SINS is based on the bound on foot-to-foot distance. It is shown how EKF inconsistency can be manifested, and how it can be avoided by proceeding back to dynamic errors. The results are obtained analytically using observability theory and covariance analysis.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic 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 Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

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