scholarly journals MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2302
Author(s):  
Kai Zhu ◽  
Xuan Guo ◽  
Changhui Jiang ◽  
Yujingyang Xue ◽  
Yuanjun Li ◽  
...  
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
State Constraints ◽  
Rapid Development ◽  
Tall Buildings ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Vehicle Positioning

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

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.

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 MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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 Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

2020 ◽  
Vol 12 (10) ◽  
pp. 1686 ◽  
Author(s):  
Xiwei Bai ◽  
Weisong Wen ◽  
Li-Ta Hsu
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
Feature Tracking ◽  
Measurement Model ◽  
Integrated Navigation ◽  
Dynamic Features ◽  
Integrated Navigation System ◽  
M Estimator ◽  
Dynamic Objects

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

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.

A Design of MIMU/GPS Integrated Navigation System

2013 ◽  
Vol 278-280 ◽  
pp. 1719-1722 ◽  
Author(s):  
Xiao Yu Zhang ◽  
Chun Lei Song
Keyword(s):  
Navigation System ◽  
Inertial Measurement Unit ◽  
High Performance ◽  
Inertial Navigation System ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Test Results ◽  
Simulation Test ◽  
Integrated Navigation System ◽  
Navigation Algorithm

A new scheme of small integrated navigation system based on micro inertial measurement unit (MIMU), global position system (GPS) is presented. The characteristic of these sensors and the structure of system are introduced respectively. The TI high performance floating point DSP TMS320C6713B is used as core processor, which is designed to realize both the data collecting and the navigation calculating. According to the error models of inertial navigation system, an integrated navigation algorithm used Kalman filter is proposed to fuse the information from all of the sensors. The simulation test results show the feasibility of the system design.

Research on the Hardware-in-the-Loop Simulation for High Dynamic SINS/GNSS Integrated Navigation System

2013 ◽  
Vol 846-847 ◽  
pp. 378-382
Author(s):  
Hao Ran Lei ◽  
Shuai Chen ◽  
Yao Wei Chang ◽  
Lei Jie Wang
Keyword(s):  
Navigation System ◽  
Dynamic Condition ◽  
Angular Rate ◽  
Rotation Vector ◽  
Measurement Unit ◽  
Hardware In The Loop ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Sample Rotation ◽  
High Dynamic

In the process of developing guided munitions, ground test can only verify the performance of integrated navigation system in low dynamic condition, and its costly and risky to use means of authentication such as flight test and throw experiment. This paper proposes a kind of hardware-in-the-loop simulation (HILS) scheme with tri-axial turntable for verifying the performance of navigation system in high dynamic condition. It respectively uses quaternion method and four-sample rotation vector algorithm as attitude updating algorithms for comparison. On the basis of analyzing the characteristics of some tactical missile and the HILS system, the error sources of integrated navigation system in the simulation with turntable and that without turntable are discussed in detail. The results of HILS show that integrated navigation system is of good performance under high dynamic environment; moreover, for the fiber optic gyroscope (FOG) inertial measurement unit (IMU) which outputs angular rate, quaternion method is better than four-sample rotation vector algorithm.

scholarly journals Multiple Model Adaptive Rank Estimation for Integrated Navigation During Mars Entry

2016 ◽  
Vol 70 (2) ◽  
pp. 291-308 ◽  
Author(s):  
Qiang Xiao ◽  
Huimin Fu ◽  
Zhihua Wang ◽  
Yongbo Zhang
Keyword(s):  
Navigation System ◽  
Uncertain System ◽  
Measurement Unit ◽  
Filter Method ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Rank Estimation ◽  
Three Degree Of Freedom

Accurate navigation systems are required for future pinpoint Mars landing missions. A radio ranging augmented Inertial Measurement Unit (IMU) integrated navigation system concept is considered for the Mars entry navigation. The uncertain system parameters associated with the Three Degree-Of-Freedom (3-DOF) dynamic model, and the measurement systematic errors are considered. In order to improve entry navigation accuracy, this paper presents the Multiple Model Adaptive Rank Estimation (MMARE) filter of radio beacons/IMU integrated navigation system. 3-DOF simulation results show that the performances of the proposed navigation filter method, 70·39 m estimated altitude error and 15·74 m/s estimated velocity error, fulfill the need of future pinpoint Mars landing missions.

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.

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