Extended Kalman Filter for GPS Receiver Position Estimation

2018 ◽  
pp. 481-488 ◽  
Author(s):  
N. Ashok Kumar ◽  
Ch. Suresh ◽  
G. Sasibhushana Rao
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Position Estimation ◽  
Gps Receiver

scholarly journals GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

2022 ◽  
Author(s):  
Sirish Kumar Pagoti ◽  
Bala Sai Srilatha Indira Dutt Vemuri ◽  
Ganesh Laveti
Keyword(s):  
Kalman Filter ◽  
Indian Subcontinent ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Low Latitude ◽  
Position Accuracy ◽  
Aircraft Landing ◽  
Gps Accuracy

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

scholarly journals Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Reliability Function ◽  
Position Estimation ◽  
Position Tracking ◽  
Radar Sensors ◽  
Vehicle Position ◽  
Sensor Errors ◽  
Fuzzy Adaptive

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

EXTENDED KALMAN FILTER BASED BRUSHLESS DC MOTOR FOR ROTOR POSITION AND SPEED CONTROL

2021 ◽  
Author(s):  
Nabiya Ellahi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Reference Signal ◽  
Dc Motor ◽  
Position Estimation ◽  
Brushless Dc Motor ◽  
Zero Crossing ◽  
Rotor Position ◽  
Brushless Dc ◽  
Improved Performance

A method to control speed and rotor position with improved performance has been described in this research. Various techniques are taken into consideration with their detailed description. During this process new methods are also introduced with their pros and cons. The research includes a detailed study of progressive back-Emf sensing strategies. The relevant methods, which can support estimation, are the back Emf zero-crossing method, integration of voltage, and position estimation by flux and inductance. In this thesis, Extended Kalman filter is utilized for position and speed estimation. Firstly, DC voltage will be applied as an input. Extended Kalman Filter is used to perform state estimation while PID controller is employed to regulate the system state following the reference signal. The proposed solution leads to control of the ripple generated in speed and torque of Brushless DC Motor and improved performance.

scholarly journals Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 182-187 ◽  
Author(s):  
Weidong Zhou ◽  
Jiaxin Hou ◽  
Lu Liu ◽  
Tian Sun ◽  
Jing Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Sensors ◽  
Integrated Navigation ◽  
Gps Receiver ◽  
Noisy Environment ◽  
Integrated Navigation System ◽  
Error Covariance ◽  
Time Update

AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.

scholarly journals Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

scholarly journals Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

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