Target Assignment Algorithm for Joint Air Defense Operation Based on Spatial Crowdsourcing Mode

Abstract

Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial crowdsourcing theory into the field of target allocation for joint air defense operations and establish a weapon-target assignment model based on spatial crowdsourcing mode, which is more appropriate to the real situation and highlights the system cooperation capability of joint air defense operations. To solve the model, we propose a heuristic variable weight nonlinear learning factor particle swarm optimization (VWNF-PSO). This algorithm can significantly improve the efficiency and adaptability to weapon-target assignment problems under large-scale extreme conditions. Finally, we establish two kinds of joint air defense operation scenarios to verify the proposed model, then compare the proposed algorithm with variable weight PSO (VWPSO) and adaptive learning factor PSO (AFPSO), to validate the effectiveness and efficiency of the VWNF-PSO algorithm proposed in this paper.