GPR antenna position and orientation estimation using strapdown inertial navigation

2000 ◽  
Author(s):  
Friedrich Roth ◽  
Gary R. Olhoeft
Keyword(s):  
Inertial Navigation ◽  
Orientation Estimation ◽  
Position And Orientation ◽  
Antenna Position ◽  
Strapdown Inertial Navigation

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.

Observability and Estimation Error Analysis of the Initial Fine Alignment Filter for Nonleveling Strapdown Inertial Navigation System

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Seong Yun Cho ◽  
Hyung Keun Lee ◽  
Hung Kyu Lee
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
The State ◽  
State Model ◽  
Low Grade ◽  
State Variables ◽  
Estimation Errors ◽  
Strapdown Inertial Navigation

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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