Relative Position Estimation of Moving Distributed Sources Using the Extended Kalman Filter

2004 ◽  
Vol 43 (5B) ◽  
pp. 3186-3187 ◽  
Author(s):  
Jong-Rak Yoon ◽  
Kyu-Chil Park ◽  
Yong-Ju Ro
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Relative Position ◽  
Position Estimation

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 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.

scholarly journals Obstacle Tracking on Unmanned Surface Vehicle Using Kalman Filter

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Rusdhianto Effendi Abdul Kadir ◽  
Mochammad Sahal ◽  
Yusuf Bilfaqih ◽  
Zulkifli Hidayat ◽  
Gaung Jagad
Keyword(s):  
Kalman Filter ◽  
System Design ◽  
Relative Position ◽  
Water Surface ◽  
Tracking System ◽  
Position Estimation ◽  
Guidance System ◽  
Light Detection And Ranging ◽  
Unmanned Surface Vehicle ◽  
Surrounding Environment

Unmanned Surface Vehicles (USV) are self-driving vehicles that operate on the water surface. In order to be operated autonomously, USV has a guidance system designed for path planning to reach its destination. The ability to detect obstacles in its paths is one of the important factors to plan a new path in order to avoid obstacles and reach its destination optimally. This research designed an obstacle tracking system which integrates USV perception sensors such as camera and Light Detection and Ranging (LiDaR) to gain information of the obstacle’s relative position in the surrounding environment to the ship. To improve the relative position estimation of the obstacles to the ship, Kalman filter is applied to reduce the measurements noises. The results of the system design are simulated using MATLAB software so that results can be analyzed to see the performance of the system design. Results obtained using the Kalman filter show 12% noise reduction. Keywords: filter kalman, obstacle tracking, unmanned surface vehicle.

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