Integrated navigation method based on inertial navigation system and Lidar

2016 ◽  
Vol 55 (4) ◽  
pp. 044102 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Haitao Shi ◽  
Jianye Pan ◽  
Chunxi Zhang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Navigation Method

scholarly journals Accurate Integrated Navigation Method Based on Medium Precision Strapdown Inertial Navigation System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yang Bo ◽  
Yang Xiaogang ◽  
Qu Geping ◽  
Wang Yongjun
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Star Sensor ◽  
Integrated Navigation ◽  
Measurement Equation ◽  
Navigation Method ◽  
Better Than ◽  
Attitude Matrix

A method of accurate integrated navigation for high-altitude aerocraft by medium precision strapdown inertial navigation system (SINS), star sensor, and global navigation satellite system (GNSS) is researched in this paper. The system error sources of SINS and star sensor are analyzed and modeled, and then system errors of SINS and star sensor are chosen as system states of integrated navigation. Considering that the output of star sensor is attitude quaternion, it can be regarded as an attitude matrix, then the equivalent attitude matrix is constructed by using the output of SINS, and the calculating equation of the equivalent attitude matrix is designed. Thus, one of the measurements of integrated navigation can be constructed by using the equivalent attitude matrix and the attitude matrix output of star sensor. According to the constraint conditions of the attitude matrix, the diagonal elements are selected as one of the measurements of integrated navigation, and the corresponding measurement equation is derived. At the same time, the velocity output and position output difference between SINS and GNSS is selected as the other measurement, and the corresponding measurement equation is also derived. On this basis, the Kalman filter is used to design an integrated navigation filtering algorithm. Simulation results show that although the medium precision SINS is used, the heading accuracy of this integrated navigation method is better than ±1.5′, the pitch and roll accuracy are better than ±0.9’, the velocity accuracy is better than ±0.05 m/s, and the position accuracy is better than ±3.8 m. Therefore, the integrated navigation effect is very significant.

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.

Ocean Vehicle Inertial Navigation Method based on Dynamic Constraints

2018 ◽  
Vol 71 (6) ◽  
pp. 1553-1566
Author(s):  
Jiazhen Lu ◽  
Lili Xie
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
External Support ◽  
External Information ◽  
Identification Algorithm ◽  
Dynamic Identification ◽  
Dynamic Constraints ◽  
Attitude Error ◽  
Navigation Method

This paper proposes a dynamic aided inertial navigation method to improve the attitude accuracy for ocean vehicles. The proposed method includes a dynamic identification algorithm and the utilisation of dynamic constraints to derive additional observations. The derived additional observations are used to update the filters and limit the attitude error based on the dynamic knowledge. In this paper, two dynamic conditions, constant speed cruise and quasi-static, are identified and corresponding additional velocity and position observations are derived. Simulation and experimental results show that the proposed method can improve and guarantee the accuracy of the attitude. The method can be used as a backup method to bridge external information outages or unavailability. Both the features of independence of external support and integrity of the Inertial Navigation System (INS) are enhanced.

scholarly journals An Integration Method of Inertial Navigation System and Three-Beam Lidar for the Precision Landing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Pengbo Liu ◽  
Chunxi Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integration Method ◽  
Inertial Navigation ◽  
Measurement Model ◽  
Reconstruction Method ◽  
Integrated Navigation ◽  
Fault Resistance ◽  
Vehicle Test

To ensure the high accuracy, independence, and reliability of the measurement system in the unmanned aerial vehicle (UAV) landing process, an integration method of inertial navigation system (INS) and the three-beam Lidar is proposed. The three beams of Lidar are, respectively, regarded as an independent sensor to integrate with INS according to the conception of multisensor fusion. Simultaneously, the fault-detection and reconstruction method is adopted to enhance the reliability and fault resistance. First the integration method is described. Then the strapdown inertial navigation system (SINS) error model is introduced and the measurement model of SINS/Lidar integrated navigation is deduced under Lidar reference coordinate. The fault-detection and reconstruction method is introduced. Finally, numerical simulation and vehicle test are carried out to demonstrate the validity and utility of the proposed method. The results indicate that the integration can obtain high precision navigation information and the system can effectively distinguish the faults and accomplish the reconstruction to guarantee the normal navigation when one or two beams of the Lidar malfunction.

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