Adaptive selection of Anchors in the Extended Kalman Filter tracking algorithm

2014 ◽  
Author(s):  
Jorge Juan Robles ◽  
Gregory Cardenas-Mansilla ◽  
Ralf Lehnert
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Algorithm ◽  
Adaptive Selection ◽  
Selection Of

scholarly journals Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Zuquan Xiang ◽  
Tao Tao ◽  
Lifei Song ◽  
Zaopeng Dong ◽  
Yunsheng Mao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Navigation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Similarity Function ◽  
Unmanned Surface Vehicle ◽  
Feature Map ◽  
Center Position ◽  
Self Organizing

The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.

scholarly journals Position and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization

2021 ◽  
Vol 54 (4) ◽  
pp. 559-568
Author(s):  
Samia Allaoui ◽  
Yahia Laamari ◽  
Kheireddine Chafaa ◽  
Salah Saad
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Great Influence ◽  
Correct Selection ◽  
State Variables ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Trial And Error Method ◽  
Error Method ◽  
Selection Of

In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBO-EKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSO-EKF approaches either in resolution or in convergence speed.

An Extended Kalman Filter Based Object Tracking Algorithm for Autonomous Guided Truck Using 1-Layer Laser Scanner

2018 ◽  
Author(s):  
Kwangseok Oh ◽  
Taejun Song ◽  
Hyewon Lee
Keyword(s):  
Mathematical Model ◽  
Performance Evaluation ◽  
Kalman Filter ◽  
Object Tracking ◽  
Extended Kalman Filter ◽  
Laser Scanner ◽  
Tracking Algorithm ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Point Data

This paper describes an extended Kalman filter based object tracking algorithm for autonomous guided truck using 1-layer laser scanner. The 1-layer laser scanner has been used to obtain 2D cloud point data to detect the preceding object for tracking control. The object tracking algorithm proposed in this study consists of perception, decision, and control stages. In the perception stage, object’s information such as relative coordinate and yaw angle has been derived based on coordinate transformation, clustering, and state estimation algorithm using the obtained point data from laser scanner. In order to estimate object’s states such as coordinate and velocity, the extended Kalman filter has been used in this study. Based on the estimated states of the object, the desired path has been derived for calculation of steering angle. The simplified mathematical model of the truck has been derived to design optimal controller. The optimal controller designed in this study is based on the linear quadratic regulator for computing the optimal angle of steering module used for tracking. In order for reasonable performance evaluation, actual data from laser scanner and the derived mathematical model of truck have been used. The developed tracking algorithm and performance evaluation have been designed and conducted on Matlab/Simulink environment. Results of the performance evaluation show that the developed object tracking algorithm has been able to track the preceding object using 1-layer laser scanner.

Reconstructing Nonlinear Dynamics by Extended Kalman Filtering

1998 ◽  
Vol 08 (03) ◽  
pp. 557-569 ◽  
Author(s):  
David M. Walker ◽  
Alistair I. Mees
Keyword(s):  
Nonlinear Dynamics ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Minimum Description Length ◽  
Subset Selection ◽  
Extended Kalman Filtering ◽  
Experimental Systems ◽  
Function Models ◽  
Selection Of

We investigate the use of the extended Kalman filter as a tool for the parameter estimation of radial basis function models. We show that the method is best used as an add-on to other estimation techniques such as subset selection of the centers using a minimum description length criterion rather than as a stand-alone method. We also show that the covariance matrix returned by the extended Kalman filter can be used to calculate Rissanen's minimum description length criterion more easily. Illustrative examples are given for data from the Ikeda map and two time series of real world experimental systems.

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