A Novel Heterogeneous Sensor-Weapon-Target Cooperative Assignment for Ground-to-Air Defense by Efficient Evolutionary Approaches

Zhang, Kai; Zhou, Deyun; Yang, Zhen; Kong, Weiren; Zeng, Lei

doi:10.1109/access.2020.3043667

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…Environmental modeling mainly involves building geographical models [4][5][6][7] and quantifying threats [8][9][10][11][12]. In the literature [4], an elevation map was used to simulate the actual flight environment of UAVs and construct a trajectory optimization method.…”

Section: Related Workmentioning

confidence: 99%

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UAV Swarm Search Path Planning Method Based on Probability of Containment

Fan,

Li,

Chen

et al. 2024

Drones

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To improve the search efficiency of the unmanned aerial vehicle (UAV) swarm in disaster areas, the target distribution probability graph in the prior information is introduced, and a drone cluster search trajectory planning method based on probability of containment (POC) is proposed. Firstly, based on the concept of probability of containment in search theory, a task area division method for polygonal and circular areas is constructed, and the corresponding search trajectory is constructed. Then, the influence of factors, including probability of containment, probability of detection, and probability of success on search efficiency, is sorted out, and the objective function of search trajectory optimization is constructed. Subsequently, an adaptive mutation operator is used to improve the differential evolution algorithm, thus constructing a trajectory optimization process based on the improved adaptive differential evolution algorithm. Through simulation verification, the proposed method can achieve a full coverage search of the task area and a rapid search within a limited time, and can prioritize the coverage of areas with a high target existence probability as much as possible to achieve a higher cumulative success probability. Moreover, the time efficiency and accuracy of the solution are high.

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Section: Related Workmentioning

confidence: 99%

“…Threats usually include radar and missile threats. To study the impacts of these military equipment and UAV trajectory planning, the literature [8] constructed a threat model for radar and missiles. Based on this, the literature [9] further extended it to the 3D space.…”

Section: Related Workmentioning

confidence: 99%

UAV Swarm Search Path Planning Method Based on Probability of Containment

Fan,

Li,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers have been trying to use artificial intelligence for planning in order to reduce the burden of commanders and improve speed and efficiency. But artificial intelligence is mainly used for tactical decision making, such as target classification [1][2][3], behavior recognition [4], threat assessment and target assignment [5,6], fire support of the maneuver operation [7], and so on. For combat-level mission planning, there is little research on the application of machine learning because it is difficult to obtain training datasets.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Song

Zhang

Zhao

et al. 2022

Discrete Dynamics in Nature and Society

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Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.

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“…At present, WTA has been applied in a variety of military scenarios, such as base defense resource assignment [21,22], multi-stage weapon target assignment [23][24][25], sensorweapon-target assignment [26][27][28][29], and antagonistic weapon target assignment [30]. In these applications, antagonistic environment is the current research hotspot.…”

Section: Introductionmentioning

confidence: 99%