Estimation and tracking of a moving target by unmanned aerial vehicles

Jun Ming Li, Ching Wen Chen, Teng-Hu Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


An image-based control strategy along with estimation of target motion is developed to track dynamic targets without motion constraints. To the best of our knowledge, this is the first work that utilizes a bounding box as image features for tracking control and estimation of dynamic target without motion constraint. The features generated from a You-Only-Look-Once (YOLO) deep neural network can relax the assumption of continuous availability of the feature points in most literature and minimize the gap for applications. The challenges are that the motion pattern of the target is unknown and modeling its dynamics is infeasible. To resolve these issues, the dynamics of the target is modeled by a constant-velocity model and is employed as a process model in the Unscented Kalman Filter (UKF), but process noise is uncertain and sensitive to system instability. To ensure convergence of the estimate error, the noise covariance matrix is estimated according to history data within a moving window. The estimated motion from the UKF is implemented as a feedforward term in the developed controller, so that tracking performance is enhanced. Simulations are demonstrated to verify the efficacy of the developed estimator and controller.

Original languageEnglish
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538679265
StatePublished - 1 Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: 10 Jul 201912 Jul 2019

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States


  • Estimation
  • Tracking of moving targets
  • UAV
  • Unscented kalman filter


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