Study on ZUPT Technology Applied in SINS

2012 ◽  
Vol 546-547 ◽  
pp. 1360-1365
Author(s):  
Xing Xing Dai ◽  
Ling Xie ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Error Control ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Velocity Error ◽  
Zero Velocity ◽  
Position Errors

Zero Velocity Update (ZUPT) is an essential method of error control in Stapdown Inertial Navigation System (SINS), which is extensively used because of its cheapness and efficiency. ZUPT uses the output of velocity error of SINS when the carrier is parking, to update the errors of other items in SINS. This method can improve the position and direction precisions of SINS. Kalman filter is chosen as the method of ZUPT to correct the velocity and position errors in SINS in this article. The method of ZUPT based on Kalman filter is applied to the vehicle experiment. The results of the vehicle experiment indicate that the ZUPT based on Kalman filter is efficient and powerful in error control, and the Kalman filter designed based on SINS is proper.

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.

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.

Design and Simulation of an Off-Line Internal Navigation System for Pipeline Inspection Applications

2007 ◽  
Author(s):  
Mohammad Durali ◽  
Ali Nabi ◽  
Amir Fazeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Reasonable Accuracy ◽  
Pipe Inspection ◽  
Pipeline Inspection ◽  
Position Errors ◽  
Suitable Reference

The aim of this paper is to design an inertial navigation system (INS) for use in a geometry pipe inspection gauge, capable of measuring pipeline movements and producing the line’s 3D map with a reasonable accuracy. A suitable reference path was generated as a design platform. Solving the navigation equations and compensating for the errors, by using extended Kalman filter (EKF) approach, the INS path was generated and its position errors in all three directions were considered. Divergence problems due to far apart GPS position observations, was overcome by defining suitable threshold for the variances of the estimated errors.

scholarly journals Bionic Integrated Positioning Mechanism Based on Bioinspired Polarization Compass and Inertial Navigation System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1055
Author(s):  
Qingyun Zhang ◽  
Jian Yang ◽  
Panpan Huang ◽  
Xin Liu ◽  
Shanpeng Wang ◽  
...  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
System Model ◽  
Degree Of Polarization ◽  
Positioning System ◽  
Integration Model ◽  
Position Errors ◽  
Model Combining ◽  
Polarization Compass

In this paper, to address the problem of positioning accumulative errors of the inertial navigation system (INS), a bionic autonomous positioning mechanism integrating INS with a bioinspired polarization compass is proposed. In addition, the bioinspired positioning system hardware and the integration model are also presented. Concerned with the technical issue of the accuracy and environmental adaptability of the integrated positioning system, the sun elevation calculating method based on the degree of polarization (DoP) and direction of polarization (E-vector) is presented. Moreover, to compensate for the latitude and longitude errors of INS, the bioinspired positioning system model combining the polarization compass and INS is established. Finally, the positioning performance of the proposed bioinspired positioning system model was validated via outdoor experiments. The results indicate that the proposed system can compensate for the position errors of INS with satisfactory performance.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Incremental predictive Kalman filter for alignment of inertial navigation system

2018 ◽  
pp. 095441001879432
Author(s):  
Mahdi Fathi ◽  
Nematollah Ghahramani ◽  
Mohammad Ali Shahi Ashtiani ◽  
Ali Mohammadi ◽  
Mohsen Fallah
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation
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