Rapid transfer alignment of an inertial navigation system using a marginal stochastic integration filter

2017 ◽  
Vol 29 (1) ◽  
pp. 015105
Author(s):  
Dapeng Zhou ◽  
Lei Guo
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Stochastic Integration ◽  
Transfer Alignment ◽  
Rapid Transfer

scholarly journals Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

2019 ◽  
Vol 69 (4) ◽  
pp. 320-327
Author(s):  
Hongde Dai ◽  
Juan Li ◽  
Liang Tang ◽  
Xibin Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Performance Comparison ◽  
Measurement Equation ◽  
Packet Dropping ◽  
Transfer Alignment ◽  
Moving Base ◽  
Independent Identically Distributed ◽  
Rapid Transfer

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy.

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.

The Effectiveness of Acceleration Matching According to the Sensor Performance in Shipboard Rapid Transfer Alignment

2019 ◽  
Vol 73 (1) ◽  
pp. 1-15
Author(s):  
Hojin Ju ◽  
Seong Yun Cho ◽  
Chan Gook Park
Keyword(s):  
Angular Velocity ◽  
Navigation System ◽  
Performance Index ◽  
Inertial Navigation System ◽  
The Other ◽  
Transfer Alignment ◽  
Sensor Performance ◽  
Velocity Information ◽  
Simulation Results ◽  
Rapid Transfer

In this study, the effect of acceleration matching according to sensor specifications in rapid transfer alignment is analysed. In general, the velocity and attitude information of the Master Inertial Navigation System (MINS) is used for transfer alignment. MINS angular velocity information is used to improve the alignment speed in shipboard transfer alignment. Acceleration matching, on the other hand, is generally considered an impractical option for transfer alignment. However, in the case of shipboard transfer alignment, acceleration matching is thought to be effective. In order to analyse the performance of acceleration matching, a performance index is defined and the efficiency of acceleration matching is analysed according to various sensor specifications and simulation environments. Based on the analysis of the estimated performance according to the simulation results, it is confirmed that acceleration matching in rapid transfer alignment is valid.

Application of Attitude Matrix Row Element Sum Matching in Transfer Alignment

2013 ◽  
Vol 336-338 ◽  
pp. 506-512
Author(s):  
Ju Feng Wang ◽  
Nong Cheng
Keyword(s):  
Equation Of State ◽  
Navigation System ◽  
Inertial Navigation System ◽  
State Space Model ◽  
Inertial Navigation ◽  
The State ◽  
Alignment Accuracy ◽  
Transfer Alignment ◽  
Attitude Angle ◽  
Attitude Matrix

To reduce the calculation brought by attitude angle and attitude matrix matching as well as to insure the transfer alignment accuracy, the paper gives a method that uses slave inertial navigation system (SINS) error model as the state-space model. The complexity of the state equation can be simplified by this method, for the lever-arm effect can be compensated in the observables rather than considering it in the equation of state, in which the inputs do not need to be brought in the equation of state; On the basis of element matching of the attitude matrix with SINS and master inertial navigation system (MINS), the paper raises row elements sum matching method of attitude matrix, which has simpler structure of the observation matrix and lower dimension compared to attitude angle matching and element matching of the attitude matrix, so as to decrease filtering computation. Simulation results have proved that the model and matching algorithm in this paper are effective and the system can achieve higher transfer alignment accuracy.

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.

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