This paper explores the problem of cooperative control among multiple networked unmanned air vehicles (UAVs) for a persistent area denial (PAD) mission. An adaptive Markov chain model is used to predict the locations of pop-up threats. The mixed information of predicted pop-up threats and actual pop-up targets is utilized to develop cooperative strategies for networked UAVs. The approach is illustrated by use of a simulation test bed for multiple networked UAVs and Monte Carlo simulation runs to evaluate the cooperative strategy set forth here. Both theoretical analysis and simulation results are presented to demonstrate the effectiveness of using predicted pop-up information in improving the overall PAD mission performance.
Coordinating Networked Uninhabited Air Vehicles for Persistent Area Denial
2004
7 pages
Report
No indication
English
Aircraft , Statistical Analysis , Military Sciences , Detection & Countermeasures , Control systems , Networks , Threats , Position finding , Cooperation , Surveillance drones , Area denial , Markov processes , Computerized simulation , Integer programming , Estimates , Targeting , Adaptive systems , Mathematical prediction , Monte carlo method , Ground level , Uncertainty , Symposia , Cooperative control , Persistent area denial , Pop-up threats , Uav(Unmanned aerial vehicles) , Markov chain models , Multiple uavs , Pad(Persistent area denial) , Iads(Integrated air defense systems) , Ground targets , Pop-up targets , Ground threats , Hidden threats , Monte carlo simulation
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