Improved precision of strapdown inertial navigation system brought by dual-axis continuous rotation of inertial measurement unit

2010 ◽  
Author(s):  
An Li ◽  
Guo-bin Chang ◽  
Fang-jun Qin ◽  
Hong-wu Li
Keyword(s):  
Navigation System ◽  
Inertial Measurement Unit ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Continuous Rotation ◽  
Strapdown Inertial Navigation

Optimal Sensor Fusion in Redundant Inertial Measurement Unit

2013 ◽  
Vol 433-435 ◽  
pp. 250-253 ◽  
Author(s):  
Xiao Qiang Dai ◽  
Lin Zhao ◽  
Zhen Shi
Keyword(s):  
Sensor Fusion ◽  
Navigation System ◽  
Inertial Measurement Unit ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Compensation Algorithm ◽  
Drift Compensation

In order to improve the reliability and accuracy of inertial navigation system in some navigation applications, the redundant inertial measurement unit (RIMU) is used. In this study, an optimal sensor fusion is introduced, and drift compensation based on this sensor fusion is presented subsequently. And the sensor fusion and drift compensation are combined into one algorithm. In this algorithm, faulty drift sensors are not isolated, but are compensated. So the redundant inertial navigation system can maintain systems redundancy. An errors model of RIMU was derived firstly, the optimal sensor fusion and the drift compensation algorithm were introduced secondly, and several simulations were carried out and proved effective of the proposed algorithm lastly.

Fault Detection Method Applied to a Redundant Strapdown Inertial Navigation System

2011 ◽  
Vol 179-180 ◽  
pp. 1242-1247 ◽  
Author(s):  
Yu Rong Lin ◽  
Si Yan Guo ◽  
Guang Ying Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Detection Method ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Measurement Unit ◽  
Detection Scheme ◽  
Vector Error ◽  
Strapdown Inertial Navigation

A fault detection method applied to a redundant strapdown inertial navigation system, which usually undergoes rapid maneuvers, is developed in this paper. First, an improved four-points detection scheme that can significantly reduce the probability of false alarm of the generalized likelihood test(GLT) is present. Then, based on analyzing influences on the fault detection performance caused by the misalignment and scale fator errors and the random bias of a gyroscope, a parity vector error model is constructed and sequently the Kalman filtering scheme to compensate the parity vector error is designed. By example of a redundant measurement unit with four single-freedom-degree gyros, the fault detection method has been analyzed qualitatively and quantitatively through simulation tests. Simulation results demonstrate the favorable performance of the method.

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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