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

Abstract

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.

Cooperative Occupancy Decision Making of Multi-UAV in Beyond-Visual-Range Air Combat: A Game Theory Approach

“…The dominance value of each UAV in M over each UAV in N can be calculated from formula (8), and the maximum expected dominance value f * R (s R , s B ) under strategy profile (s R , s B ) can be obtained by solving MR. By the same process, f * B (s R , s B ) can also be obtained.…”

“…Due to the limited number and performance of the weapons and sensors carried by a single UAV, its ability to carry out air combat tasks is limited. The effective coordination of multiple UAVs can better accomplish air combat tasks [8]- [10]. Recently, the cooperative BVR air combat decision making of multiple UAVs has attracted increasing attention [11]- [13].…”

Analysis of GPR Wave Propagation Using CUDA‐Implemented Conformal Symplectic Partitioned Runge‐Kutta Method

“…General purpose graphics processing unit (GPGPU) technology has higher computing power and lower equipment cost [31][32][33], which provides a good parallel computing platform for the numerical simulation of GPR with complex underground structures. At present, research on GPU acceleration is mainly focused on acoustics [34], biomedical applications [35], computational electromagnetics [36], and other applications. A parallel symplectic algorithm based on GPU has not been developed.…”