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Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition
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School of Mechanical and Control Engineering, Handong Global University, Pohang, Republic of Korea
College of Aerospace, Beijing Institute of Technology, Beijing 100081, China
Department of Physical and Technological Oceanography, Institut de Ciències del Mar (ICM), Consejo Superior de Investigaciones Científicas (CSIC), 08003 Barcelona, Spain
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Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition

Abstract submission deadline
20 May 2025
Manuscript submission deadline
20 August 2025
Viewed by
427

Topic Information

Dear Colleagues,

Growing civilian and military demand for autonomous systems, including unmanned vehicles, has promoted the development of modern target tracking, guidance, and navigation technologies. Target information is vital for autonomous systems to interact with their surrounding environments, enabling them to complete their missions. However, the motion of the system itself affects the quality of target information, which implies that the target-tracking problem is inseparable from the guidance and navigation of autonomous systems. Modern technologies such as model-/data-driven estimation, heterogeneous data fusion, optimization, and artificial intelligence can improve target-tracking systems and, subsequently, change the overall performance of guidance and navigation. This Special Issue aims to identify recent theoretical and technical advances in target tracking, guidance, and navigation, which provide autonomous systems with a high degree of autonomy. Related topics include, but are not limited to:

  • Tracking maneuvering targets in cluttered/jammed environments;
  • Joint target tracking and classification using heterogenous sensors;
  • Centralized/distributed multi-sensor fusion;
  • Optimal sensor arrangement;
  • Guidance, navigation, and control of autonomous vehicles;
  • Integrated target tracking and guidance;
  • Dynamic model-based navigation;
  • Swarm localization;
  • Dynamic object tracking using SLAM (simultaneous localization and mapping);
  • Applied artificial intelligence in target tracking, guidance, and navigation.

Prof. Dr. Won-Sang Ra
Prof. Dr. Shaoming He
Dr. Ivan Masmitja
Topic Editors

Keywords

  • target tracking
  • target classification
  • heterogeneous sensor fusion
  • sensor arrangement
  • autonomous navigation
  • autonomous vehicle guidance
  • swarm localization
  • applied artificial intelligence

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Aerospace
aerospace 		2.6 3.0 2014 22.3 Days CHF 2400 Submit
Automation
automation 		- - 2020 26.3 Days CHF 1000 Submit
Drones
drones 		4.8 6.1 2017 17.9 Days CHF 2600 Submit
Remote Sensing
remotesensing 		5.0 7.9 2009 23 Days CHF 2700 Submit
Sensors
sensors 		3.9 6.8 2001 17 Days CHF 2600 Submit

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Published Papers (1 paper)

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22 pages, 4523 KiB  
Article
Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization
by Xiaobo Zheng, Feiran Xia, Defu Lin, Tianyu Jin, Wenshan Su and Shaoming He
Aerospace 2024, 11(6), 420; https://doi.org/10.3390/aerospace11060420 - 22 May 2024
Viewed by 222
Abstract
Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the [...] Read more.
Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the trajectory to pass through intermediate waypoints and optimizing the flight time between these waypoints. A constrained parameterized differential dynamic programming (C-PDDP) algorithm is proposed for meeting multiple waypoint constraints and free-time constraints between waypoints to deal with these two issues. By considering the intermediate waypoint constraints as a kind of path state constraint, the penalty function method is adopted to constrain the trajectory to pass through the waypoints. For the free-time constraints, the flight times between waypoints are converted into time-invariant parameters and updated at the trajectory instants corresponding to the waypoints. The effectiveness of the proposed C-PDDP algorithm under waypoint constraints and free-time constraints is verified through numerical simulations of the UAV multi-point reconnaissance problem with five different waypoints. After comparing the proposed algorithm with fixed-time constrained DDP (C-DDP), it is found that C-PDDP can optimize the flight time of the trajectory with three segments to 7.35 s, 9.50 s and 6.71 s, respectively. In addition, the maximum error of the optimized trajectory waypoints of the C-PDDP algorithm is 1.06 m, which is much smaller than that (7 m) of the C-DDP algorithm used for comparison. A total of 500 Monte Carlo tests were simulated to demonstrate how the proposed algorithm remains robust to random initial guesses. Full article
(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)
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