The effect of external interference on the performance of navigation radar

Arisesa, Hana; Rahman, Arief Nur; Putranto, Prasetyo; Desvasari, Winy; K, Deni Permana; Daud, Pamungkas; Hardiati, Sri

doi:10.1109/icramet.2016.7849582

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Two UAV types form one group and select the overall shortest path to cooperatively strike and destroy three different mission targets from the base. It could be observed from Figure 7 that in the initial plan, the targets 1, 2, and 3 are struck cooperatively by the JUAV and CUAV through selecting the bases (1, 8), (5,6), and (4, 7) as their respective initial bases. The total value of the objective function is 488.2233 km (Table 2).…”

Section: Simulation Processmentioning

confidence: 99%

“…It is seen in Figure 8 that the targets 1, 2, and 3 are cooperatively struck by the JUAV and CUAV through selecting the bases (1,3), (4,7), and (6,8) as their respective initial bases. The total value of the objective function is 425.8619 km (Table 2).…”

Section: Simulation Processmentioning

confidence: 99%

“…Therefore, it is extremely difficult to execute missions such as reconnaissance, destruction, and strikes only based on a single UAV. In practice, multi-type UAVs are required to improve the success rate of the target missions based on extensive cooperation between different types of UAVs [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram‐Tabu Genetic Algorithm

Tan

Zhao

et al. 2021

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

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Section: Simulation Processmentioning

confidence: 99%