Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	3.4	2014	24 Days	CHF 2400	Submit
Automation automation	-	2.9	2020	20.6 Days	CHF 1000	Submit
Drones drones	4.4	5.6	2017	21.7 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit

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Published Papers (3 papers)

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All Aerospace Automation Drones Remote Sensing Sensors

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15 pages, 2330 KiB  

Open AccessArticle

Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

by Tianning Wang, Zhu Wang, Wei Li and Chao Liu

Aerospace 2024, 11(11), 895; https://doi.org/10.3390/aerospace11110895 - 30 Oct 2024

Viewed by 371

Abstract

For the cooperative reconnaissance assignment of unmanned aerial vehicles (UAVs) on multiple targets, this paper presents a flexible combinatorial-bids-based auction (FCBA) method that can optimize the number of UAVs for each target. Considering the reconnaissance effectiveness enhancement achieved with cooperative observation and the [...] Read more.

For the cooperative reconnaissance assignment of unmanned aerial vehicles (UAVs) on multiple targets, this paper presents a flexible combinatorial-bids-based auction (FCBA) method that can optimize the number of UAVs for each target. Considering the reconnaissance effectiveness enhancement achieved with cooperative observation and the time-critical characteristic of targets, the multitarget assignment problem is formulated as a nonlinear integer optimization to maximize the cooperative effectiveness. To achieve target assignment without predetermining the number of UAVs for each target, a combinatorial bidding framework is proposed, and an allocation method for rewards and costs among the cooperative UAVs is constructed. Strategies for auction iteration and bid updating are also designed to acquire equilibrium results under the combinatorial bidding mechanism. The simulation results show that the proposed method can generate satisfactory suboptimal results according to the enumerated solutions. A comparison of the results demonstrates that the FCBA can provide comparable optimal results to a genetic algorithm but has better computational efficiency, and the reconnaissance effectiveness can be improved by considering cooperative observation. Full article

(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)

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24 pages, 5021 KiB  

Open AccessArticle

A Robust Tri-Electromagnet-Based 6-DoF Pose Tracking System Using an Error-State Kalman Filter

by Shuda Dong and Heng Wang

Sensors 2024, 24(18), 5956; https://doi.org/10.3390/s24185956 - 13 Sep 2024

Viewed by 587

Abstract

Magnetic pose tracking is a non-contact, accurate, and occlusion-free method that has been increasingly employed to track intra-corporeal medical devices such as endoscopes in computer-assisted medical interventions. In magnetic pose-tracking systems, a nonlinear estimation algorithm is needed to recover the pose information from [...] Read more.

Magnetic pose tracking is a non-contact, accurate, and occlusion-free method that has been increasingly employed to track intra-corporeal medical devices such as endoscopes in computer-assisted medical interventions. In magnetic pose-tracking systems, a nonlinear estimation algorithm is needed to recover the pose information from magnetic measurements. In existing pose estimation algorithms such as the extended Kalman filter (EKF), the 3-DoF orientation in the $S^3$ manifold is normally parametrized as unit quaternions and simply treated as a vector in the Euclidean space, which causes a violation of the unity constraint of quaternions and reduces pose tracking accuracy. In this paper, a pose estimation algorithm based on the error-state Kalman filter (ESKF) is proposed to improve the accuracy and robustness of electromagnetic tracking systems. The proposed system consists of three electromagnetic coils for magnetic field generation and a tri-axial magnetic sensor attached to the target object for field measurement. A strategy of sequential coil excitation is developed to separate the magnetic fields from different coils and reject magnetic disturbances. Simulation and experiments are conducted to evaluate the pose tracking performance of the proposed ESKF algorithm, which is also compared with standard EKF and constrained EKF. It is shown that the ESKF can effectively maintain the quaternion unity and thus achieve a better tracking accuracy, i.e., a Euclidean position error of 2.23 mm and an average orientation angle error of 0.45°. The disturbance rejection performance of the electromagnetic tracking system is also experimentally validated. Full article

(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)

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22 pages, 4528 KiB  

Open AccessArticle

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

Cited by 1 | Viewed by 1074

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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