Xiaolong Jin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaolong Jin has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bo Hou
Rocket Force University of Engineering, Xi’an 710025, China
1 shared publication - Haiyang Wang
Rocket Force University of Engineering, Xi’an 710025, China
1 shared publication - Zhiliang Fan
Rocket Force University of Engineering, Xi’an 710025, China
1 shared publication - Zhongjie Yin
College of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China
1 shared publication
Latest Publications
- Open Access
MPC-Based Dynamic Trajectory Spoofing for UAVs
Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods.
Open AccessMPC-Based Dynamic Trajectory Spoofing for UAVs
Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods.