An Automatic Acquisition Method of Lead Planes Based on Particle Swarm Optimization

In a cooperative air combat, the whole command and control system (CCS) may lose control if the lead plane in the middle of the fleet is damaged or fails. In order to solve this problem, this article simulates a dynamic and global optimum CCS for the cooperative air combat with the particle swarm optimization (PSO) algorithm and automatically determines the lead plane by adding "lead bird" selection factors to the basic algorithm. Finally, the experiments verify this method is feasible and reliable. Multi-plane air combat command and control structure is generally classified into three levels: organization, coordination and execution, which definitely specifies the authorities and ranks of command and control. When the lead plane in the middle of the fleet cannot command the fleet due to damages and malfunction, the whole command and control system (CCS) shall lose control . Literature [2] suggests the airborne early warning (AEW) airplane to take the overall command and control centralized control in the whole theater of operations. Such command structure can solve the loss of control due to damages or failures of the lead plane, but it significantly reduce the autonomous offensive of single airplane because the whole data link requires more time to communicate when all commands are issued by the early warning airplane. In Literature [3], the author put forward the concept and structural frames of the cooperative air combat CCS. Literature [4] introduced a model of the air combat command and control system Eagle Vision used by US Army, but did not give the specific analysis thereof. The cooperative combat is very similar with the behaviors of bird groups . Therefore, the article adopts swarm intelligence-based PSO algorithm to simulate an overall and dynamic cooperative air combat CSS and achieve automatic acquisition of lead plane by adding the "lead bird" selection factors to the basic algorithm. 1. Automatic acquisition model of lead plane based on PSO 1.1 Air fleet system model based on PSO There is a mapping relation between PSO and fleet, so we can properly modify and expand PSO and determine the movement direction, speed and expected position of the individual using PSO of Reynolds model, in which the value of outside signals detected by the airplane is the adaptive value and the plane in the communication range as the perception neighborhood . Meanwhile, calculate the movement position of the plane in real time with communication modes, communication cycles and sampling cycles, etc. The flight path should be planned if there is any barrier in the operation area and the size of the plane is considered. Individual optimization and overall optimization depend on the mission schedule. Accordingly, we can take PSO model as the behavior control model of the airplanes to coordinate and control movement behaviors of the airplanes, achieving swarm intelligence. The equation of PSO-based modeling is indicated as follows: