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.

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 A Novel Information Fusion Method for Redundant Rotational Inertial Navigation Systems Based on Reduced-Order Kalman Filter

2018 ◽  
Vol 160 ◽  
pp. 07005
Author(s):  
Lin Wang ◽  
Wenqi Wu ◽  
Guo Wei ◽  
Jinlong Li ◽  
Ruihang Yu
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
High Reliability ◽  
Inertial Navigation ◽  
Position Error ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Reduced Order

The redundant rotational inertial navigation systems can satisfy not only the high-accuracy but also the high-reliability demands of underwater vehicle on navigation system. However, different systems are usually independent, and lack of information fusion. A reduced-order Kalman filter is designed to fuse the navigation information output of redundant rotational navigation systems which usually include a dual-axis rotational inertial navigation system being master system and a single-axis rotational inertial navigation system being hot-backup system. The azimuth gyro drift of single-axis rotational inertial navigation system can be estimated by the designed filter, whereby the position error caused by that can be compensated with the aid of designed position error prediction model. As a result, the improved performance of single-axis rotational inertial navigation system can guarantee the position accuracy in the case of dual-axis system failure. Semi-physical simulation and experiment verify the effectiveness of the proposed method.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

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.

Initial Alignment of a Moving Inertial Navigation System

1960 ◽  
Vol 13 (3) ◽  
pp. 301-315
Author(s):  
Richard B. Seeley ◽  
Roy Dale Cole
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Local Level ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Velocity Measurements ◽  
Initial Alignment ◽  
Inertial Navigation Systems ◽  
Error Sources ◽  
China Lake

This paper describes and discusses some of the techniques by which a moving inertial platform may be aligned by using external velocity measurements and also presents some of the major problems and error sources affecting such alignment. It is based upon the results of a 3-year study, of inertial and doppler-inertial navigation at the Naval Ordnance Test Station, China Lake, California, and, in general, applies to inertial navigation systems which erect to either the local level or the mass-attraction vertical. Although rudimentary derivations are made of the alignment techniques, the paper is largely nonmathematical for ease of reading. Emphasis is placed upon the major errors affecting the alignment. This paper describes and discusses some of the techniques by which a moving inertial platform may be aligned by using external velocity measurements and also presents some of the major problems and error sources affecting such alignment. It is based upon the results of a 3-year study, of inertial and doppler-inertial navigation at the Naval Ordnance Test Station, China Lake, California, and, in general, applies to inertial navigation systems which erect to either the local level or the mass-attraction vertical. Although rudimentary derivations are made of the alignment techniques, the paper is largely nonmathematical for ease of reading. Emphasis is placed upon the major errors affecting the alignment.

Problem of the Vertical Deflection in High-Precision Inertial Navigation

2020 ◽  
Vol 28 (4) ◽  
pp. 3-15
Author(s):  
V.G. Peshekhonov ◽  
◽  
Keyword(s):  
Systematic Error ◽  
High Precision ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Reference Ellipsoid ◽  
Navigation Systems ◽  
Vertical Deflection ◽  
Inertial Navigation Systems ◽  
Output Data

The paper addresses the systematic error of an inertial navigation system, caused by the discrepancy between the plumb line and the normal to the reference ellipsoid surface. The methods of this discrepancy estimation, and their use for correcting the output data of inertial navigation systems are studied.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

scholarly journals Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

2013 ◽  
Vol 66 (5) ◽  
pp. 751-772 ◽  
Author(s):  
Xueyun Wang ◽  
Jie Wu ◽  
Tao Xu ◽  
Wei Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Cost Effective ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Position Accuracy

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

Fast Fine Initial Self-alignment of INS in Erecting Process on Stationary Base

2017 ◽  
Vol 71 (3) ◽  
pp. 697-710 ◽  
Author(s):  
Jianli Li ◽  
Yiqi Li ◽  
Baiqi Liuxs
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Euler Angle ◽  
Alignment Method ◽  
State Variables ◽  
Initial Alignment ◽  
Angle Error ◽  
Alignment Model ◽  
System Observability

Fine initial alignment is vital to the Inertial Navigation System (INS) before the launching of a missile. The existing initial alignment methods are mainly performed on a stationary base after the missile has been erected to the vertical state. However, these methods consume extra alignment time and some state variables have poor degrees of observability, thus losing the rapidity of alignment. In order to solve the problem, a fast fine initial self-alignment method of a missile-borne INS is proposed, which is performed during the erecting process on a stationary base. The convected Euler angle error is modelled to optimise the erecting manoeuvre which can prevent large Euler angle errors and improve the system observability. The fine initial alignment model is established to estimate and correct the initial misalignment. Several experiments verify that the proposed method is effective for improving the rapidity of the fine initial alignment for a missile-borne INS.

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