Rapid Transfer Alignment Using Integrated Navigation System as Reference

2015 ◽  
Vol 713-715 ◽  
pp. 1156-1165
Author(s):  
Wei Sheng ◽  
Xiao Han ◽  
Ding Sheng Sun
Keyword(s):  
Navigation System ◽  
The Body ◽  
Integrated Navigation ◽  
Body Frame ◽  
Time Duration ◽  
Transfer Alignment ◽  
Integrated Navigation System ◽  
Attitude Error ◽  
Long Time ◽  
The Impact

Traditional transfer alignment must use a pure Inertial Navigation System (INS) as the reference, because using the integrated navigation system as the reference would disrupt the transfer alignment process and lead to the divergence of the fine alignment filter. But there are some cases require the cooperation between the master INS and slave INS for long time duration, such as mapping. To guarantee the accuracy of the master INS, integrated navigation feedback is needed. So, transfer alignment technique using the integrated navigation system as the reference is required. Taking the error of master INS into account, a transfer alignment attitude error in the body frame was defined, which makes the transfer alignment attitude error free from the impact of integrated navigation feedback corrections. Then, its error equations were developed and a Kalman filter was designed based on it. Finally, a transfer alignment simulation using a GPS/INS integrated navigation system as reference was performed to validate the proposed scheme.

scholarly journals Airborne Integrated Navigation System Based on SINS/GPS/TAN/EOAN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junjun Tang ◽  
Peijuan Li
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Integrated Navigation ◽  
Long Distance ◽  
Integrated Navigation System ◽  
Long Time ◽  
Federated Kalman Filter ◽  
Entropy Weighted ◽  
Navigation Errors ◽  
Navigation Information

Considering the drawbacks that GPS signal is susceptible to obstacles and TAN becomes useless in some area when without any terrain data or with a featureless terrain field, to realize long-distance and high-precision navigation, a navigation system based on SINS/GPS/TAN/EOAN is presented. When GPS signal is available, GPS is used to correct errors of SINS; when GPS is unavailable, a terrain selection method based on the entropy weighted gray relational decision-making method is use to distinguish terrain into matchable areas and unmatchable areas; then, for the matchable areas, TAN is used to correct errors of SINS, for the unmatchable areas, EOAN is used to correct errors of SINS. The principles of SINS, GPS, TAN, and EOAN are analyzed, the mathematic models of SINS/GPS, SINS/TAN, and SINS/EOAN are constructed, and finally the federated Kalman filter is used to fuse navigation information. Simulation results show that the trajectory of SINS/GPS/TAN/EOAN is close to the ideal one in both matchable area or unmatchable area and whose navigation errors are obviously reduced, which is important for the realization of long-time and high-precision positioning.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

Application of Kalman Filter Technology to Integrated INS/DR Navigation System for Land Vehicle

2013 ◽  
Vol 411-414 ◽  
pp. 912-916 ◽  
Author(s):  
Ying Chen ◽  
Xia Jiang Zhang ◽  
Yuan Yuan Xue ◽  
Zhen Kang ◽  
Ting Shang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dead Reckoning ◽  
Position Error ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Attitude Error ◽  
Result Show ◽  
The Difference

Strap-down INS is composed of fiber gyroscope. Position error propagation equation and position update algorithm of dead reckoning is deduced in this paper. The Kalman filter is proposed for compensation error of integrated system. The difference of velocity between INS and DR is used as the input of Kalman filter, attitude error, velocity error, position error and scale factor error are to be estimated which compensate and rectify the errors of integrated navigation system. By carrying out experiment upon vehicular navigation system in use of Kalman filter, the errors of integrated navigation system are estimated accurately. Experiment result show that the method not only can effectively improve precision of the system, but also is simple and convenient, so it is more suitable for practical application.

Transfer Alignment for MEMS Integrated Navigation System Based on H∞ Filter

2014 ◽  
Vol 490-491 ◽  
pp. 886-890
Author(s):  
Xing Zhi Zhang ◽  
Kun Peng He ◽  
Chen Yang Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Digital Simulation ◽  
Integrated Navigation ◽  
Misalignment Angle ◽  
Transfer Alignment ◽  
Integrated Navigation System ◽  
Simulation Results

The transfer alignment of strapdown inertial units were proposed that use the H filter to estimate the misalignment of the slave INS (inertial navigation system) relative to the master INS. Characteristics of the H filter in transfer alignment were studied in detail by checking digital simulation results obtained by using the H and Kalman filters. The results shows that the misalignment angle obtained with the H filter converge faster and closer to the exact values than do those obtained with the Kalman filter. The H filter is more robust than the Kalman filter in transfer alignment for MEMS integrated navigation system.

On SINS\Star Tracker Geo-localization

2021 ◽  
Author(s):  
Nassim Bessaad ◽  
Qilian Bao ◽  
Zhao Jiankang ◽  
Karam Eliker
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Integrated System ◽  
Star Tracker ◽  
Integrated Navigation ◽  
Initial Alignment ◽  
Static Mode ◽  
Integrated Navigation System ◽  
The Dead ◽  
The Impact

Abstract This work focuses on the feasibility of a fully autonomous geo-localization system for near-earth applications based on the strap-down inertial navigation system (SINS) and the star tracker. First, each sensor is analyzed individually. Then, the performance of the integrated system in a dynamic situation is investigated. Moreover, a detailed angle error analysis is given to estimate the impact on geo-localization. The navigation solution is proven to be affected by the sensors' errors plus an algorithmic error from the dead reckoning computation. Lastly, simulations are concluded to assess the dynamic movement scenario's performance and navigational possibility using the nonlinear Kalman filter. The results show the continuing divergence of the integrated navigation system affected by the dead reckoning algorithm. However, the continuous initial alignment in static mode reinitializes the position error successfully.

Sign in / Sign up

Export Citation Format

Share Document