scholarly journals An Improved Adaptive Compensation H∞ Filtering Method for the SINS’ Transfer Alignment Under a Complex Dynamic Environment

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 401 ◽  
Author(s):  
Weiwei Lyu ◽  
Xianghong Cheng ◽  
Jinling Wang
Keyword(s):  
Inertial Sensors ◽  
Dynamic Environment ◽  
Inertial Navigation ◽  
Adaptive Compensation ◽  
Complex Dynamic ◽  
Transfer Alignment ◽  
Filtering Method ◽  
Balance Accuracy ◽  
Alignment System ◽  
Moving Base

Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.

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.

Attitude Matching for Gimbaled Inertial Navigation System Transfer Alignment

2012 ◽  
Vol 190-191 ◽  
pp. 768-773
Author(s):  
Zhi Jian Ding ◽  
Hong Cai ◽  
Hua Bo Yang ◽  
Yuan Cao
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Angle Error ◽  
Transfer Alignment ◽  
Moving Base ◽  
Alignment Model

Abstract: Aiming at transfer alignment of gimbaled INS(Inertial Navigation Systems) on moving base, the paper proposes an attitude matching alignment model to calibrate the slave platform. This method is achieved by applying a Kalman filter, which based on the frame angle error equations, to estimate the fixed misalignment angle and obtain the misalignment angle. Firstly, the frame dynamics equations are introduced and the relation between the fixed angle and misalignment angle is discussed. Secondly, the frame angular error differential equations are built up via the frame angle information from the master and the slave INS platform. Lastly, the attitude matching alignment model is designed based on Kalman filter technology. The simulation results show that the proposed method can obtain an alignment accuracy of 40", and the corresponding alignment time is 30 seconds.

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.

MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE

2010 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lucian T. Grigorie ◽  
Ruxandra M. Botez
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Lower Cost ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Noise Power ◽  
Integration Algorithm ◽  
The Cost ◽  
Miniaturized Sensors

In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.

Analysis of the Symmetry for a Rotating Strapdown Inertial Navigation System

2012 ◽  
Vol 566 ◽  
pp. 703-706
Author(s):  
Wei Gao ◽  
Ya Zhang ◽  
Qian Sun ◽  
Yue Yang Ben
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Basic Requirement ◽  
Position Accuracy ◽  
Rotation Method ◽  
Constant Bias ◽  
Strapdown Inertial Navigation

It is known that the precision of the strapdown inertial navigation system is influenced by constant bias of inertial sensors. A method of self-compensation based on a rotating inertial navigation system is proposed to enhance the precision. The constant drift of gyro and accelerometers is modulated into a seasonal and zero-mean form. In the paper, the theory of the rotary modulation and the basic requirement of the rotation method are analyzed. A new dual-axis rotating method is put forward. Simulations have been done. And the results indicate that the method can clear up the constant bias of the inertial sensors quickly and effectively. The position accuracy can be greatly enhanced compared with no rotary manner.

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