English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate estimation and prediction of trajectory is essential for the captureof any high speed target. In this paper, an extended Kalman filter (EKF) isused to track the target in the first loop of the trajectory to collect datapoints and then a combination of machine learning with least-squarecurve-fitting is used to accurately estimate future positions for thesubsequent loops. The EKF estimates the current location of target from itsvisual information and then predicts its future position by using theobservation sequence. We utilize noisy visual information of the target fromthe three dimensional trajectory to carry out the predictions. The proposedalgorithm is developed in ROS-Gazebo environment and is implemented onhardware.

Accurate Prediction and Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method