A Time Delay Compensation Algorithm for Transfer Alignment of Strap-Down Inertial Navigation System

2014 ◽  
Vol 909 ◽  
pp. 288-292 ◽  
Author(s):  
Run Wu Zhong ◽  
Shuai Chen ◽  
Yu Kun Wang
Keyword(s):  
Time Delay ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Delay Compensation ◽  
Transfer Alignment ◽  
Compensation Algorithm ◽  
System Data ◽  
Time Delay Compensation ◽  
The Moment

When SINS (slave strapdown inertial navigation system) got the MINS (Master Inertial Navigation System) data, the time reference was unified to the moment corresponding to the MINS data, and the data was processed by K'alman filter,which effects convergence speed and precision of airborne navigation system.In order to improve the precision of transfer alignment for airborne navigation system ,a compensation algorithm for information transmission time delay[1] is proposed to estimate the delay time and use relevant data to compensate the errors caused by time delay.A method using velocity and measurement misalignment angle information for processing the time delay was derived.A simulation system was made to validate the calculation.And the simulation results show the feasibility and effectiveness of this compensation algorithm.

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.

Optimisation-based Transfer Alignment and Calibration Method for Inertial Measurement Vector Integration Matching

2016 ◽  
Vol 70 (3) ◽  
pp. 595-606 ◽  
Author(s):  
Lili Xie ◽  
Jiazhen Lu
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Prior Information ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Inertial Measurement ◽  
Transfer Alignment ◽  
Sensor Error ◽  
Vector Integration ◽  
Measurement Vector

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

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