Comparison of inertial navigation system error models in application to IMU transfer alignment

1997 ◽  
Author(s):  
Robert Rogers ◽  
Robert Rogers
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
System Error ◽  
Error Models ◽  
Transfer Alignment

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.

An Adaptive Level Damped Algorithm Based on PINS for Ship

2013 ◽  
Vol 760-762 ◽  
pp. 2162-2166
Author(s):  
Pei Yu ◽  
Gong Liu Yang
Keyword(s):  
Theoretical Analysis ◽  
Real Time ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
System Error ◽  
Sea Current

The inertial navigation system error caused by sea current and log error greatly oscillates. In order to evaluate the effectiveness of damping attitude, the vehicles movement should be detected in real-time. For this reason, a novel adaptive level damped algorithm was presented in this paper. According to the characteristics of the movement of the ship, this algorithm has determined when to turn into the damp loop according to the change of acceleration. Theoretical analysis and Simulations results show that the adaptive level damped algorithm could damp most of the Schuler and Foucault oscillations period, and efficiently improve the precision of the PINS on ships.

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.

Sign in / Sign up

Export Citation Format

Share Document