scholarly journals Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ji Hyoung Ryu ◽  
Ganduulga Gankhuyag ◽  
Kil To Chong
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Navigation Systems ◽  
Position Information ◽  
Global Positioning ◽  
Heading Angle ◽  
Electronic Compass

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Supervised Machine Learning ◽  
Positioning System ◽  
Position Information ◽  
Global Positioning

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

On Navigation Systems for Motorcycles: The Influence and Estimation of Roll Angle

2005 ◽  
Vol 58 (3) ◽  
pp. 375-388 ◽  
Author(s):  
Joshua P. Coaplen ◽  
Patrick Kessler ◽  
Oliver M. O'Reilly ◽  
Dan M. Stevens ◽  
J. Karl Hedrick
Keyword(s):  
Global Positioning System ◽  
Angular Rate ◽  
Roll Angle ◽  
Estimation Methods ◽  
Navigation Systems ◽  
Simulation Environment ◽  
Global Positioning ◽  
Heading Angle ◽  
Navigation Information ◽  
Navigation Scheme

Vehicle navigation systems use various sensors and the global positioning system (GPS) to locate a vehicle. This location is then matched to a map database to provide navigation information. Between GPS updates, the vehicle's heading angle and forward speed are used to “dead reckon” its position. Heading angle is often measured by integrating the output of a rate gyroscope. For this measurement to be equal to the vehicle's heading angle, the vehicle should not experience any rotation about its roll or pitch axes. For an automobile, the roll and pitch angles are small and may be neglected for the purposes of navigation. This article demonstrates that this same assumption is not true for a motorcycle. Through simulation, it is shown that for a motorcycle, obtaining a meaningful heading angle from a single angular rate measurement requires accounting for the motorcycle's roll angle. Methods to estimate roll angle and heading angle from available navigation measurements are presented, and two possible sensor configurations are compared. A motorcycle navigation scheme based on these roll angle estimation methods is shown to produce exceptional results in a simulation environment.

scholarly journals A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

2015 ◽  
Vol 15 (6) ◽  
pp. 294-303 ◽  
Author(s):  
Zhibin Miao ◽  
Hongtian Zhang ◽  
Jinzhu Zhang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Vehicle Stability ◽  
Positioning System ◽  
Robust Method ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Global Positioning

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Low cost automation using INS/GPS data fusion for accurate positioning

Robotica ◽  
2003 ◽  
Vol 21 (3) ◽  
pp. 255-260 ◽  
Author(s):  
J. Z. Sasiadek ◽  
Q. Wang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Real Time Control ◽  
Adaptive Kalman Filter ◽  
Time Control ◽  
Global Positioning ◽  
And Control ◽  
Fuzzy Adaptive

Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

scholarly journals Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

2016 ◽  
Vol 70 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Chien-Hao Tseng ◽  
Sheng-Fuu Lin ◽  
Dah-Jing Jwo
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Measurement Information ◽  
Gps Navigation ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
M Estimation ◽  
Non Gaussian ◽  
Distribution Assumption

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

scholarly journals Nonlinear Filtering for Hybrid GPS/GSM Mobile Terminal Tracking

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Carsten Fritsche ◽  
Anja Klein
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Mobile Terminal ◽  
Localization Method ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Hybrid Localization ◽  
Time Information

The Global Positioning System (GPS) has become one of the state-of-the-art location systems that offers reliable mobile terminal (MT) location estimates. However, there exist situations where GPS is not available, for example, when the MT is used indoors or when the MT is located close to high buildings. In these scenarios, a promising approach is to combine the GPS-measured values with measured values from the Global System for Mobile Communication (GSM), which is known as hybrid localization method. In this paper, three nonlinear filters, namely, an extended Kalman filter, a Rao-Blackwellized unscented Kalman filter, and a modified version of the recently proposed cubature Kalman filter, are proposed that combine pseudoranges from GPS with timing advance and received signal strengths from GSM. The three filters are compared with each other in terms of performance and computational complexity. Posterior Cramér-Rao lower bounds are evaluated in order to assess the theoretical performance. Furthermore, it is investigated how additional GPS reference time information available from GSM influences the performance of the hybrid localization method. Simulation and experimental results show that the proposed hybrid method outperforms the GSM method.

A Dual-IMU/GPS based Geolocation System

2011 ◽  
Vol 65 (1) ◽  
pp. 113-123 ◽  
Author(s):  
Jong Ki Lee ◽  
Christopher Jekeli
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Unexploded Ordnance ◽  
Gps Receiver ◽  
Detection Systems ◽  
Kalman Smoother ◽  
Global Positioning ◽  
Measurement Units ◽  
The Common

To improve the geolocation performance of an Unexploded Ordnance (UXO) survey platform, a geodetic Global Positioning System (GPS) receiver was combined with two tactical-grade Inertial Measurement Units (IMUs) and mounted on two types of detection systems. Analysis of data collected for typical trajectories focused on the dual-IMU/GPS pre/post processing using optimal nonlinear estimation together with a Wave Correlation Filter (WCF) and end-matching. Each trajectory of the platforms was estimated by the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The WCF was then applied to the two solutions of the platform trajectories derived from each IMU in order to extract the common components in the frequency domain, assuming that uncorrelated components are errors. The remaining bias and trends of the estimated position results were further removed by end-matching IMU solutions and GPS update points. The results of these methods were applied to our field test data to show that the WCF and end-matching can improve position accuracy from 4% to 14% with respect to the Unscented Kalman Smoother (UKS) solution alone.

scholarly journals The Use of Kalman Filter Techniques for Ship Track Estimation

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Dynamic Systems ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Positioning System ◽  
Global Positioning ◽  
Systems Identification ◽  
Simulation Results ◽  
Marine Vessels

Unscented Kalman Filter (UKF) is a technique used in non-linear applications and dynamic systems identification (e.g. tracking marine vessels and ships) that require state and parameter estimation. This paper studies Kalman Filter (KF) based techniques for tracking ships using Global Positioning System (GPS) data. The present work proposes to exploit information from GPS sensors in order to track a ship in real-time. The absence and presence problem of a ship is handled by a applying KF theory to analyze GPS coordinates and compare current marine vessel routes to previously recorded ones. To study tracking performance, the system was implemented in C++ and simulation results demonstrate the feasibility and high accuracy of the proposed tracking method

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