The Kalman Filter in Transfer Alignment of Airborne Inertial Guidance Systems

1968 ◽  
Author(s):  
Jr. Sutherland ◽  
Gelb Arthur A. ◽  
Arthur
Keyword(s):  
Kalman Filter ◽  
Transfer Alignment ◽  
Guidance Systems

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

scholarly journals Study on a Twice Transfer Alignment Based on Dual Model

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Shuai Chen ◽  
Runwu Zhong ◽  
Xiaohui Liu ◽  
Ahmed Alsaedi
Keyword(s):  
Kalman Filter ◽  
Convergence Time ◽  
Alignment Algorithm ◽  
Dual Model ◽  
Matching Method ◽  
Error Models ◽  
Transfer Alignment ◽  
Nonlinear Error ◽  
Dual Models ◽  
Rapid Transfer

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

Velocity and Attitude Matching of Transfer Alignment by Using H∞ Filter

2012 ◽  
Vol 433-440 ◽  
pp. 4861-4864 ◽  
Author(s):  
Li Jun Song ◽  
Deng Feng Cheng ◽  
Wei Lu
Keyword(s):  
Kalman Filter ◽  
White Noise ◽  
Estimation Method ◽  
Measurement Noise ◽  
Engineering Practice ◽  
Transfer Alignment ◽  
Color Noise ◽  
System Noise ◽  
Simulation Results

The H∞ filter is adopted in the transfer alignment (TA) which is realized by the Velocity and Attitude Matching, when the disturbances in measurements are complete unknown. The performance of H∞ filter is compared with kalman filter. The simulation results show both that H∞ filter and kalman filter all are effective and kalman filter is more accurate than H∞ filter when system noise and measurement noise are white noise. But H∞ filter is more accurate than kalman filter when system noise and measurement noise are color noise. H∞ filter is an effective estimation method because H∞ filter is more suitable to engineering practice than kalman filter.

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