scholarly journals Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 546
Author(s):  
Jiaxin Gao ◽  
Kui Li ◽  
Jiyang Chen
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Integrated System ◽  
Integrated Navigation ◽  
Fiber Optic Gyroscope ◽  
Velocity Difference ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Positioning Errors ◽  
Over Time

Autonomous and accurate acquisition of the position and azimuth of the vehicle is critical to the combat effectiveness of land-fighting vehicles. The integrated navigation system, consisting of a strap-down inertial navigation system (SINS) and odometer (OD), is commonly applied in vehicles. In the SINS/OD integrated system, the odometer is installed around the vehicle’s wheel, while SINS is usually installed on the base of the vehicle. The distance along SINS and OD would cause a velocity difference when the vehicle maneuvers, which may lead to a significant influence on the integration positioning accuracy. Furthermore, SINS navigation errors, especially azimuth error, would diverge over time due to gyro drifts and accelerometer biases. The azimuth error would cause the divergence of dead-reckoning positioning errors with the distance that the vehicle drives. To solve these problems, an integrated positioning and orientation method based on the configuration of SINS and couple odometers was proposed in this paper. The proposed method designed a high precision integrated navigation algorithm, which compensated the lever arm effect to eliminate the velocity difference between SINS and odometers. At the same time, by using the measured information of couple odometers, azimuth reference was calculated and used as an external measurement to suppress SINS azimuth error’s divergence over time, thus could further improve the navigation precision of the integrated system, especially the orientation accuracy. The performance of the proposed method was verified by simulations. The results demonstrated that SINS/2ODs integrated system could achieve a positioning accuracy of 0.01% D (total mileage) and orientation accuracy of ±30″ by using SINS with 0.01°/h Fiber-Optic Gyroscope (FOGs) and 50 µg accelerometers.

Application of Kalman Filter Technology to Integrated INS/DR Navigation System for Land Vehicle

2013 ◽  
Vol 411-414 ◽  
pp. 912-916 ◽  
Author(s):  
Ying Chen ◽  
Xia Jiang Zhang ◽  
Yuan Yuan Xue ◽  
Zhen Kang ◽  
Ting Shang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dead Reckoning ◽  
Position Error ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Attitude Error ◽  
Result Show ◽  
The Difference

Strap-down INS is composed of fiber gyroscope. Position error propagation equation and position update algorithm of dead reckoning is deduced in this paper. The Kalman filter is proposed for compensation error of integrated system. The difference of velocity between INS and DR is used as the input of Kalman filter, attitude error, velocity error, position error and scale factor error are to be estimated which compensate and rectify the errors of integrated navigation system. By carrying out experiment upon vehicular navigation system in use of Kalman filter, the errors of integrated navigation system are estimated accurately. Experiment result show that the method not only can effectively improve precision of the system, but also is simple and convenient, so it is more suitable for practical application.

Design of A Filtering Algorithm Based on FOG SINS /GPS/DR

2013 ◽  
Vol 367 ◽  
pp. 499-502
Author(s):  
Ke Zhang ◽  
Jun Qing Bai ◽  
Bing Lun Gu
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Failure Detection ◽  
Integrated Navigation ◽  
Fiber Optic Gyroscope ◽  
Integrated Navigation System ◽  
Filtering Algorithm ◽  
Global Positioning ◽  
Navigation Accuracy ◽  
Better Than

As the most important index, the navigation accuracy and reliability is used to measure the performance of FOG vehicle integrated navigation system. In the foundation of federated filter and systematic accident diagnose algorithms, a novel integrated navigation filtering algorithm is designed based on FOG SINS(Fiber Optic Gyroscope Strapdown Inertial Navigation System)、GPS(Global Positioning System)、DR(Dead Reckoning). The simulation results show that the federated filter with failure detection can make full use of information from navigation sensor, and can isolate fault sources effectively, its accuracy and reliability are better than the generalized filters’.

On SINS\Star Tracker Geo-localization

2021 ◽  
Author(s):  
Nassim Bessaad ◽  
Qilian Bao ◽  
Zhao Jiankang ◽  
Karam Eliker
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Integrated System ◽  
Star Tracker ◽  
Integrated Navigation ◽  
Initial Alignment ◽  
Static Mode ◽  
Integrated Navigation System ◽  
The Dead ◽  
The Impact

Abstract This work focuses on the feasibility of a fully autonomous geo-localization system for near-earth applications based on the strap-down inertial navigation system (SINS) and the star tracker. First, each sensor is analyzed individually. Then, the performance of the integrated system in a dynamic situation is investigated. Moreover, a detailed angle error analysis is given to estimate the impact on geo-localization. The navigation solution is proven to be affected by the sensors' errors plus an algorithmic error from the dead reckoning computation. Lastly, simulations are concluded to assess the dynamic movement scenario's performance and navigational possibility using the nonlinear Kalman filter. The results show the continuing divergence of the integrated navigation system affected by the dead reckoning algorithm. However, the continuous initial alignment in static mode reinitializes the position error successfully.

scholarly journals Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2776 ◽  
Author(s):  
Mostafa Mostafa ◽  
Shady Zahran ◽  
Adel Moussa ◽  
Naser El-Sheimy ◽  
Abu Sesay
Keyword(s):  
Navigation System ◽  
Visual Odometry ◽  
Satellite System ◽  
Integrated System ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Gnss Measurements ◽  
Global Navigation Satellite

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

scholarly journals GPS/INS Integrated Navigation System for Autonomous Robots

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Tran Ngoc Huy ◽  
Le Manh Cam ◽  
Nguyen Thanh Nam
Keyword(s):  
Navigation System ◽  
Autonomous Robots ◽  
Low Cost ◽  
Dead Reckoning ◽  
Experimental System ◽  
Velocity Estimation ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Short Period

Nowadays, autonomous robots are capable of replacing people with hard work or in dangerous environments, so this field is rapidly developing. One of the most important tasks in controlling these robots is to determine its current position. The Global Positioning System (GPS) was originally developed for military purposes but is now widely used for civilian purposes such as mapping, navigation for land vehicles, marine, etc. However, GPS has some disadvantages, like the update rate is low or sometimes the satellites’ signal is suspended. Another navigation system is the Inertial Navigation System (INS) can allow you to determine position, velocities, and attitude from the subject’s status, like acceleration and rotation rate. Essentially, INS is a dead-reckoning system, so it has a huge cumulative error. An effective method is to integrate GPS with INS, in which the center is a nonlinear estimator (e.g., the Extended Kalman filter) to determine the navigation error, from which it can update the position the object more accurately. To improve even better accuracy, this paper proposes a new method that combines the original integrated GPS/INS with tri-axis rotation angles estimation and velocity constraints. The experimental system is built on a low-cost IMU with a tri-axis gyroscope, accelerometer, and magnetometer, and a GPS module to verify the model algorithm. Experiment results have shown that the rotation angles estimator helps us to determine the Euler angles correctly, thereby increasing the quality of the position and velocity estimation. In practice, the accuracy of the roll and pitch angle is 2 degrees; the error of the yaw angle is still large. The achieved horizontal accuracy is 2m when the GPS signal is stable and 3m when the GPS signal is lost in a short period. Compared with individual GPS, the error of the integrated system is about 10% smaller.

A Single Chip Integrated Navigation System

1993 ◽  
Vol 46 (1) ◽  
pp. 95-104 ◽  
Author(s):  
Eric Aardoom ◽  
André Nieuwland
Keyword(s):  
Navigation System ◽  
System Integration ◽  
Cost Effective ◽  
Integrated System ◽  
Integrated Navigation ◽  
Single Chip ◽  
Integrated Navigation System ◽  
System Integrity ◽  
Cost Effective Approach ◽  
Application Specific

Recently, integration of different radionavigation systems has become very popular, since it improves system integrity, availability, accuracy and reliability. This paper discusses a new, flexible and cost-effective approach to system integration, centred on a single-chip application specific processor (ASP). An overview of this integrated system is presented and the application of the ASP for the implementation of a six-channel GPS, OMEGA, Loran-C and MLS receiver is given. The ASP is currently being implemented on a 180000 transistor 1·6μ, m CMOS Sea of Gates chip, and is expected to run at 100 MHz clock speed.

Seamless INS/GPS Integration Based on Support Vector Machines

2013 ◽  
Vol 336-338 ◽  
pp. 277-280 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Support Vector Machines ◽  
Global Positioning System ◽  
Navigation System ◽  
Superior Performance ◽  
Support Vector ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Vector Machines ◽  
Global Positioning

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

2013 ◽  
Vol 347-350 ◽  
pp. 1544-1548
Author(s):  
Zi Yu Li ◽  
Yan Liu ◽  
Ping Zhu ◽  
Cheng Ying
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Integrated System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Non Linear ◽  
Federated Kalman Filter

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

A Ship GPS/DR Integrated Navigation System

2012 ◽  
Vol 591-593 ◽  
pp. 1818-1821
Author(s):  
Yuan Liang Zhang
Keyword(s):  
Data Fusion ◽  
Navigation System ◽  
Global Economy ◽  
Dead Reckoning ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Gps Receiver ◽  
Integrated Navigation System ◽  
Ship Navigation ◽  
Navigation Information

The development of the global economy stimulates the exploitation of the ocean. A precise and stable ship navigation system is very important for people to explore the ocean. Dead reckoning (DR) system is a frequently used navigation system for sailing in the ocean. It can provide precise short term navigation information but the error of DR system can accumulate over time without limitation. GPS can be used for localization and navigation in outside environment. Although the SA policy was removed the accuracy of GPS for civilian use is still big. But the errors of GPS are bounded. Since the complementarity of DR and GPS system the integrated GPS/DR system can provide good navigation results. In this paper a new Kalman filter based DR/GPS data fusion method is proposed. This method is designed based on the characteristic of the GPS receiver. By using this data fusion method the cheap GPS receiver can cooperate with DR system to provide precise navigation information for ships. Simulation is conducted to validate the proposed fusion method. The good result shows the potential of this fusion method for the ship navigation.

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