Maneuver decision for cooperative close-range air combat based on state predicted influence diagram

Pan, Qian; Zhou, Deyun; Huang, Jianguo; Lv, Xiaofeng; Yang, Zhen; Zhang, Kun; Li, Xiaoyang

doi:10.1109/icinfa.2017.8079001

Cited by 26 publications

(21 citation statements)

References 7 publications

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“…The results are shown in Table 4. We can find that at least 25,000 (7 6 ) elements of a column (compared with the GTX285 and Q9450) to meet the requirement. It is equivalent to six-to-six cluster battle simulation.…”

Section: Performance Evaluation Using Gpgpumentioning

confidence: 94%

“…In addition, when there are more than six aircraft on both sides, the storage capacity of the entire strategic matrix is too large (about 12 Gigabytes), which cannot be achieved on current equipment. Therefore, we only perform one row of operations and multiply 7 6 to estimate the total calculation time. It can be roughly equal to the time when the optimal strategy is calculated in 7 6 × 7 6 cluster.…”

Section: Performance Evaluation Using Gpgpumentioning

confidence: 99%

“…Therefore, we only perform one row of operations and multiply 7 6 to estimate the total calculation time. It can be roughly equal to the time when the optimal strategy is calculated in 7 6 × 7 6 cluster. When the two clusters reach eight-to-eight, the computing time of each column is about 108.97 times that of the CPU.…”

Section: Performance Evaluation Using Gpgpumentioning

confidence: 99%

“…Based on the influence diagram (ID), the research on the one-on-one air combat model can be referred to [3,4]. The literature [5,6] further proposes the ID improvement model of multi-aircraft cooperative air combat maneuver decisions. The combination auction theory is used to control the decision of multiple fighters to cooperate in close air combat, which can be seen in [7].…”

Section: Introductionmentioning

confidence: 99%

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Study on Consulting Air Combat Simulation of Cluster UAV Based on Mixed Parallel Computing Framework of Graphics Processing Unit

Kung¹

2018

Electronics

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This paper combines matrix game theory with negotiating theory and uses U-solution to study the framework of the consulting air combat of UAV cluster. The processes to determine the optimal strategy in this paper follow three points: first, the UAV cluster are grouped into fleets; second, the best paring for the joint operations of the fleet member with the enemy fleet members are calculated; thirdly, consultations within the fleet are conducted to discuss the problems of optimal tactic, roles of main/assistance, and situational assessment within the fleet. In order to improve the computing efficiency of the framework, this article explores the use of the NVIDIA graphics processor programmed through MATLAB mixed C++/CUDA toolkit to accelerate the calculations of equations of motion of unmanned aerial vehicles, the prediction of superiority values and U values, computations of consultation, the evaluation of situational assessment and the optimal strategies. The effectiveness evaluation of GPGPU and CPU can be observed by the simulation results. When the number of team air combat is small, the CPU alone has better efficiency; however, when the number of air combat clusters exceeds 6 to 6, the architecture presented in this article can provide higher performance improvements and run faster than optimized CPU-only code.

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Section: Performance Evaluation Using Gpgpumentioning

confidence: 94%

Section: Performance Evaluation Using Gpgpumentioning

confidence: 99%

Section: Performance Evaluation Using Gpgpumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study on Consulting Air Combat Simulation of Cluster UAV Based on Mixed Parallel Computing Framework of Graphics Processing Unit

Kung¹

2018

Electronics

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“…is method selects the optimal maneuver from the action library according to the designed decision rules. References [3,4] use the influence diagram to study maneuver decision. is method regards the multilevel influence diagram as a nonlinear programming problem, which is not suitable for online planning in high dynamic combat environment.…”

Section: Introductionmentioning

confidence: 99%

Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

Xie

Ding

Wei

et al. 2021

Mathematical Problems in Engineering

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To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. The results show that the prediction accuracy of Ada-LSTM is better. In the decision-making method, the moving time optimization strategy is adopted. To solve the problem of timeliness and optimization, each control variable is divided into 9 gradients, and there are 729 control schemes in the control sequence. Through contrast pursuit simulation experiments, it is verified that the maneuver decision method combining the dynamic relational weight algorithm and moving time strategy has a better accuracy and real-time performance. In the case of using prediction and not using prediction, the adaptive countermeasure simulation is carried out with the current more advanced Bayesian inference maneuvering decision-making scheme. The results show that the UCAV maneuvering decision-making ability combined with accurate prediction is better.

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Air Combat Decision Based on Genetic Fuzzy Tree

Xue

Zhou

Ran

2021

Lecture Notes in Electrical Engineering

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Maneuver decision for cooperative close-range air combat based on state predicted influence diagram

Cited by 26 publications

References 7 publications

Study on Consulting Air Combat Simulation of Cluster UAV Based on Mixed Parallel Computing Framework of Graphics Processing Unit

Study on Consulting Air Combat Simulation of Cluster UAV Based on Mixed Parallel Computing Framework of Graphics Processing Unit

Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

Air Combat Decision Based on Genetic Fuzzy Tree

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