Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon
With the increased proliferation of drone use for many purposes, counter drone technology has become crucial. This rapid expansion has inherently introduced significant opportunities and applications. This creates applications such as aerial surveillance, delivery services, agriculture monitoring, a...
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MDPI AG
2025-07-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/7/606 |
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| author | Javier Lee Hongrui Sutthiphong Srigrarom |
| author_facet | Javier Lee Hongrui Sutthiphong Srigrarom |
| author_sort | Javier Lee Hongrui |
| collection | DOAJ |
| description | With the increased proliferation of drone use for many purposes, counter drone technology has become crucial. This rapid expansion has inherently introduced significant opportunities and applications. This creates applications such as aerial surveillance, delivery services, agriculture monitoring, and, most importantly, security operations. Due to the relative simplicity of learning and operating a small-scale UAV, malicious organizations can field and use UAVs (drones) to form substantial threats. Their interception may then be hindered by evasive manoeuvres performed by the malicious UAV (mUAV). Novice operators may also unintentionally fly UAVs into restricted airspace such as civilian airports, posing a hazard to other air operations. This paper explores predictive trajectory code and methods for the neutralisation of mUAVs by following drones, using state estimation techniques such as the extended Kalman filter (EKF) and particle filter (PF). Interception strategies and optimization techniques are analysed to improve interception efficiency and robustness. The novelty introduced by this paper is the implementation of adaptive time horizon (ATH) and velocity control (VC) in the predictive process. Simulations in MATLAB were used to evaluate the effectiveness of trajectory prediction models and interception strategies against evasive manoeuvres. The tests discussed in this paper then demonstrated the following: the EKF predictive method achieved a significantly higher neutralisation rate (41%) compared to the PF method (30%) in linear trajectory scenarios, and a similar neutralisation rate of 5% in stochastic trajectory scenarios. Later, after incorporating adaptive time horizon (ATH) and 20 velocity control (VC) measures, the EKF method achieved a 98% neutralization rate, demonstrating significant improvement in performance. |
| format | Article |
| id | doaj-art-917f0dc87f334df5be3644242812258d |
| institution | DOAJ |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-917f0dc87f334df5be3644242812258d2025-08-20T02:48:19ZengMDPI AGAerospace2226-43102025-07-0112760610.3390/aerospace12070606Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time HorizonJavier Lee Hongrui0Sutthiphong Srigrarom1Department of Mechanical Engineering, National University of Singapore, Singapore 117411, SingaporeDepartment of Mechanical Engineering, National University of Singapore, Singapore 117411, SingaporeWith the increased proliferation of drone use for many purposes, counter drone technology has become crucial. This rapid expansion has inherently introduced significant opportunities and applications. This creates applications such as aerial surveillance, delivery services, agriculture monitoring, and, most importantly, security operations. Due to the relative simplicity of learning and operating a small-scale UAV, malicious organizations can field and use UAVs (drones) to form substantial threats. Their interception may then be hindered by evasive manoeuvres performed by the malicious UAV (mUAV). Novice operators may also unintentionally fly UAVs into restricted airspace such as civilian airports, posing a hazard to other air operations. This paper explores predictive trajectory code and methods for the neutralisation of mUAVs by following drones, using state estimation techniques such as the extended Kalman filter (EKF) and particle filter (PF). Interception strategies and optimization techniques are analysed to improve interception efficiency and robustness. The novelty introduced by this paper is the implementation of adaptive time horizon (ATH) and velocity control (VC) in the predictive process. Simulations in MATLAB were used to evaluate the effectiveness of trajectory prediction models and interception strategies against evasive manoeuvres. The tests discussed in this paper then demonstrated the following: the EKF predictive method achieved a significantly higher neutralisation rate (41%) compared to the PF method (30%) in linear trajectory scenarios, and a similar neutralisation rate of 5% in stochastic trajectory scenarios. Later, after incorporating adaptive time horizon (ATH) and 20 velocity control (VC) measures, the EKF method achieved a 98% neutralization rate, demonstrating significant improvement in performance.https://www.mdpi.com/2226-4310/12/7/606UAV neutralisationinterceptiontrajectory predictionleading dronecounter drone (C-UAS)following drones |
| spellingShingle | Javier Lee Hongrui Sutthiphong Srigrarom Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon Aerospace UAV neutralisation interception trajectory prediction leading drone counter drone (C-UAS) following drones |
| title | Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon |
| title_full | Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon |
| title_fullStr | Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon |
| title_full_unstemmed | Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon |
| title_short | Trajectory Planning and Optimisation for Following Drone to Rendezvous Leading Drone by State Estimation with Adaptive Time Horizon |
| title_sort | trajectory planning and optimisation for following drone to rendezvous leading drone by state estimation with adaptive time horizon |
| topic | UAV neutralisation interception trajectory prediction leading drone counter drone (C-UAS) following drones |
| url | https://www.mdpi.com/2226-4310/12/7/606 |
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