scholarly journals Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 384 ◽  
Author(s):  
Zihui Wang ◽  
Xianghong Cheng ◽  
Jingjing Du
Keyword(s):  
Temperature Field ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Unit ◽  
Calibration Model ◽  
Navigation Systems ◽  
Moving Vehicle ◽  
Temperature Change Rate ◽  
Environment Temperature ◽  
Complex Temperature

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using 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 Scalar Method of Fault Diagnosis of Inertial Measurement Unit

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Vadym Avrutov
Keyword(s):  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Calibration Method ◽  
Quality Monitoring ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Working Capacity ◽  
Element Determination ◽  
Inertial Measurement ◽  
Algorithm Verification

The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. In accordance with scalar calibration method algorithms of fault diagnosis systems are developed. As a result of quality monitoring algorithm verification is implemented in the working capacity monitoring of IMU. A failure element determination is based on diagnostics algorithm verification and after that the reason for such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors, and misalignments angles of sensors to their data sheet certificate, kept in internal memory of computer. As a result of such comparison the conclusion for working capacity of each IMU sensor can be made and also the failure sensor can be determined.

A Rapid Field Calibrating Method for MIMU

2011 ◽  
Vol 80-81 ◽  
pp. 1140-1144
Author(s):  
Yu Bao Fan ◽  
Jie Li ◽  
Bo Wang ◽  
Xiao Chun Tian ◽  
Jun Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Operation Time ◽  
Calibration Method ◽  
Least Square ◽  
Measurement Unit ◽  
Calibration Model ◽  
Time Saving ◽  
Inertial Measurement ◽  
Field Calibration ◽  
Calibration Program

When the Micro Inertial Measurement Unit is been placed randomly in the case of stationary, the sum vectors that measured by the inertial devices configured orthogonally along three axis, are constant vectors. In view of the above objective facts, a field calibration method of micro inertial measurement unit was proposed. On the base of the establishment and optimization of calibration model, all parameters to be calibrated can be obtained through the least square by the ellipsoid fitting, with the result of high-precision field calibration for micro inertial measurement unit. Finally, a filed calibration program for micro inertial measurement unit is scheduled reasonably. The experiment results show that the method has such characteristics such as easily-operation, time-saving, higher calibration accuracy, and not depending on the baseline direction and datum offered by precision instruments. Especially, it fits for inertial measurement systems which work short time and ask for high accuracy. In addition, it can also significantly increase the measurement accuracy of micro inertial measurement system in practical application.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

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.

A self-calibration method for non-orthogonal angles of gimbals in rotational inertial navigation system based on fiber optic gyro

2017 ◽  
Vol 40 (13) ◽  
pp. 3665-3674 ◽  
Author(s):  
Zengjun Liu ◽  
Lei Wang ◽  
Wei Wang ◽  
Tianxiao Song
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Fiber Optic ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Unit ◽  
Fiber Optic Gyroscope ◽  
Self Calibration ◽  
Photoelectric Encoder ◽  
Navigation Accuracy

Rotating modulation technique is a mature method that has been widely used in the rotational inertial navigation system (RINS). Tri-axis RINS has three gimbals, and the Inertial Measurement Unit can rotate along three directions to modulate the inertial devices’ errors, so that the navigation accuracy of the system can be greatly improved. However, the outputs of attitudes are easily affected by the non-orthogonal angles of gimbals, which should be accurately calibrated and compensated. In this paper, the effects of the non-orthogonal angles on the attitudes are discussed detailed and simulations based on Matlab are conducted to verify that firstly; then, a self-calibration method based on the outputs of the fiber optic gyroscope and photoelectric encoder is proposed. Experimental results in a real tri-axis RINS show that the attitude outputs accuracy are improved from 150” to less than 10”, which verify the practicability of the calibration method proposed in this paper.

Correcting Synchronous Scanning OMIS Remote Sensing Images Using the Spatial Orientation Data of the Inertial Navigation System

2014 ◽  
Vol 513-517 ◽  
pp. 2867-2870
Author(s):  
Xiao Hu Zhou
Keyword(s):  
Remote Sensing ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Calibration Model ◽  
Image Correction ◽  
Imaging Spectrometer ◽  
Orientation Data ◽  
Corrected Image

Inertial navigation system using IMU (Inertial Measurement Unit) of the flying space positioning data POS (Position & Orientation System) synchronized scanning of the hyperspectral remote sensing OMIS (Operational Modular Imaging Spectrometer) image correction, obtaining from the IMU in sync with the attitude parameter OMIS , the coordinate transformation parameter calculation and flight attitude, according to OMIS imaging principle of mathematical calibration model, the corrected image pixel re-sampling, the image correction, and achieved better image processing results.

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