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Seungkeun Kim
Researcher at Chungnam National University
Publications - 125
Citations - 1571
Seungkeun Kim is an academic researcher from Chungnam National University. The author has contributed to research in topics: Control theory & Extended Kalman filter. The author has an hindex of 19, co-authored 123 publications receiving 1212 citations. Previous affiliations of Seungkeun Kim include Seoul National University & Cranfield University.
Papers
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Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle
TL;DR: In this paper, a nonlinear model-predictive control framework for coordinated standoff tracking by a pair of UAVs is proposed, where each UAV optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication.
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Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight
TL;DR: The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of Uavals.
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Coordinated standoff tracking of moving target groups using multiple UAVs
TL;DR: A two-phase approach is proposed as a suboptimal solution for a Non-deterministic Polynomial-time hard (NP-hard) problem, consisting of target clustering/assignment and cooperative standoff group tracking with online local replanning.
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Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter
TL;DR: In this article, a new interacting multiple model (IMM) filter is proposed for actuator fault detection, where each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters to diagnose the actuator damage with an unknown magnitude.
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Coordinated trajectory planning for efficient communication relay using multiple UAVs
TL;DR: A decentralised nonlinear model predictive trajectory planning strategy for a dynamic environment is proposed by exploiting motion estimates of vessels and states of UAVs, the trajectory planning algorithm finds a control input sequence optimising network connectivity over a certain time horizon.