INS/Odometer Integration: Positional Approach

2021 ◽  
Vol 29 (2) ◽  
pp. 110-125
Author(s):  
A.A. Golovan ◽  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Zero Velocity ◽  
Strapdown Inertial Navigation

The problem of a strapdown inertial navigation system (SINS) integration with an odometer as part of an integrated navigation system is considered. The odometer raw measurement is considered as an increment of the distance traveled along the odometer ‘measuring’ axis. Models of the integration solution components for the case of threedimensional navigation are presented, among which are the models of inertial autonomous and kinematic odometer dead reckoning (DR), models of relevant error equations, the model of SINS position aiding based on the odometer DR data and using GNSS position and velocity, wherever possible. The models comprise objective components, which do not depend on the type of the inertial sensors used and their accuracy grade, and variable components, which take into account the properties of the navigation sensors used. The integration does not require zero velocity updates, known as ZUPT correction, which are commonly used in navigation application.

Analysis of the Symmetry for a Rotating Strapdown Inertial Navigation System

2012 ◽  
Vol 566 ◽  
pp. 703-706
Author(s):  
Wei Gao ◽  
Ya Zhang ◽  
Qian Sun ◽  
Yue Yang Ben
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Basic Requirement ◽  
Position Accuracy ◽  
Rotation Method ◽  
Constant Bias ◽  
Strapdown Inertial Navigation

It is known that the precision of the strapdown inertial navigation system is influenced by constant bias of inertial sensors. A method of self-compensation based on a rotating inertial navigation system is proposed to enhance the precision. The constant drift of gyro and accelerometers is modulated into a seasonal and zero-mean form. In the paper, the theory of the rotary modulation and the basic requirement of the rotation method are analyzed. A new dual-axis rotating method is put forward. Simulations have been done. And the results indicate that the method can clear up the constant bias of the inertial sensors quickly and effectively. The position accuracy can be greatly enhanced compared with no rotary manner.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF 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 IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

Estimating Gyro Drift of Strapdown Inertial Navigation System Based on Star Sensor

2012 ◽  
Vol 566 ◽  
pp. 235-238
Author(s):  
Guang Tao Zhou ◽  
Gui Min Shi ◽  
Lei Zhang ◽  
Kai Li
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Model Simulation ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Star Sensor ◽  
Integrated Navigation ◽  
Working Principle ◽  
Gyro Drift ◽  
Strapdown Inertial Navigation

In the strapdown inertial navigation system (SINS), gyro drift will result in navigation errors. A new algorithm based on star sensor is proposed in this paper to estimate gyro drift. The paper analyzed the working principle of star sensor and the technique of estimating gyro drift. Gyro drift can be estimated through the high-precision attitude information provided by a star sensor. Kalman filter is used in the integrated navigation model. Simulation results show that the proposed algorithm can estimate gyro drift accurately and improve the precision of SINS.

scholarly journals Accurate integrated position and orientation method for vehicles based on strapdown inertial navigation system /Doppler radar

2018 ◽  
Vol 51 (9-10) ◽  
pp. 431-442 ◽  
Author(s):  
Yang Bo ◽  
Wang Yue-gang ◽  
Xue Liang ◽  
Shan Bin ◽  
Wang Bao-cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Doppler Radar ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Orientation Method ◽  
Integration Mode ◽  
Position And Orientation ◽  
Strapdown Inertial Navigation

In order to realize maneuver combat in the modern warfare, some special military vehicles require the ability of determining their position and orientation rapidly and accurately, and the position and orientation system should be highly autonomous and have strong anti-jamming capability. So a high-accuracy independent position and orientation method for vehicles that utilizes strapdown inertial navigation system/Doppler radar is presented in this article. Laser gyroscopes in strapdown inertial navigation system and Doppler radar are adopted to develop a dead-reckoning system for vehicles. Subsequently, the attitude, velocity,and position-updating algorithms of dead-reckoning system are designed. The error sources of dead-reckoning system are analyzed to establish the system error model, including the attitude error equations of the mathematical platform, velocity error equations, and position error equations. The errors of strapdown inertial navigation system and deadreckoning system are selected as system states of the integrated position and orientation method. The difference between the attitude output of strapdown inertial navigation system and that of dead-reckoning system, and the difference between the position output of strapdown inertial navigation system and that of dead-reckoning system are chosen as the measurements of integrated position and orientation. Then, Kalman filter is adopted to design the filtering algorithm of integrated position and orientation. In the end, the integrated position and orientation method is validated by simulation experiment and vehicular experiment. The experimental results show that strapdown inertial navigation system/Doppler radar integration can realize accurate positioning and orientation for a long time, and the accuracy of attitude/position integration mode is significantly higher than that of velocity/position integration mode. Therefore, the former integration mode is more suitable for accurate position and orientation for vehicles.

scholarly journals An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4577 ◽  
Author(s):  
Bo Yang ◽  
Jianxiang Xi ◽  
Jian Yang ◽  
Liang Xue
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Doppler Radar ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Vertical Direction ◽  
Strapdown Inertial Navigation System ◽  
Moving Base ◽  
The Dead ◽  
Strapdown Inertial Navigation

In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a strapdown inertial navigation system and Doppler radar, we calculated the dead reckoning, analyzed the error sources of the dead reckoning system, and established an error model. Then the errors of the strapdown inertial navigation system and dead reckoning system were treated as the states. Besides velocity information, attitude information was cleverly introduced into the alignment measurement to improve alignment accuracy and reduce alignment time. Therefore, the first measurement was the difference between the output attitude and velocity of the strapdown inertial navigation system and the corresponding signals from the dead reckoning system. In order to further improve the alignment accuracy, more measurement information was introduced by using the vehicle motion constraint, that is, the velocity output projection of strapdown inertial navigation system along the transverse and vertical direction of the vehicle body was also used as the second measurement. Then the corresponding state and measurement equations were established, and the Kalman filter algorithm was used for assisted alignment filtering. The simulation results showed that, with a moving base, the misalignment angle estimation accuracy was better than 0.5’ in the east direction, 0.4’ in the north direction, and 3.2’ in the vertical direction.

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