Indoor Integrated Navigation Algorithm Based on Photoelectric Scanning and Strapdown Inertial Navigation System

2016 ◽  
Vol 53 (10) ◽  
pp. 101201
Author(s):  
王姣 Wang Jiao ◽  
杨凌辉 Yang Linghui ◽  
黄喆 Huang Zhe ◽  
史慎东 Shi Shendong ◽  
黄东 Huang Dong
2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang

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.


2012 ◽  
Vol 566 ◽  
pp. 235-238
Author(s):  
Guang Tao Zhou ◽  
Gui Min Shi ◽  
Lei Zhang ◽  
Kai Li

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.


2012 ◽  
Vol 479-481 ◽  
pp. 2610-2615
Author(s):  
Kai Yao ◽  
Qi Dan Zhu ◽  
Bo Zhang

This paper addresses a practical problem arising in the calibration of bottom-lock doppler velocity log for the navigation of surface ships. Firstly, a dead reckoning navigation algorithm and briefly error analyze are proposed. Then, employing ship’s true trajectory and calculated trajectory, the rotational alignment offset between a bottom-lock doppler velocity log and a strapdown inertial navigation system as well as the scale factor error of the doppler velocity log can be experimentally determined using sensors commonly deployed with a vehicle in the field. It requires velocity values from the vehicle's doppler log and strapdown inertial navigation system, and absolute vehicle position fixes from a GPS receiver. Lake experiment results show that the calibration algorithm can calibrate the error parameters effectively, thus the position error decreases significantly after compensating the error parameters.


2021 ◽  
Vol 29 (2) ◽  
pp. 110-125
Author(s):  
A.A. Golovan ◽  

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.


Author(s):  
Seong Yun Cho ◽  
Hyung Keun Lee ◽  
Hung Kyu Lee

In this paper, performance of the initial fine alignment for the stationary nonleveling strapdown inertial navigation system (SDINS) containing low-grade gyros is analyzed. First, the observability is analyzed by conducting a rank test of an observability matrix and by investigating the normalized error covariance of the extended Kalman filter based on the ten-state model. The results show that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and nonleveling attitude angles of a vehicle containing the SDINS. Especially, this paper shows that the larger the attitude angles of the vehicle are, the greater the estimation errors are. Finally, it is shown that the performance of the eight-state model excluding the two unobservable state variables is better than that of the ten-state model in the fine alignment by a Monte Carlo simulation.


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