A Dynamic Task Assignment Strategy for Emitter Reconnaissance and Positioning through Use of UAV Swarms

Abstract

AbstractWith three advantages of information sharing, system survival, and cost-effective exchange, Unmanned Aerial Vehicle (UAV) swarms can perform tasks at risk, such as emitter reconnaissance and positioning. Due to high risk, it’s impossible to maintain the expected effect of inter-individual cooperation among an UAV swarm. The loss of positioning UAVs and the spatiotemporal agility of a target model will also reduce the accuracy in positioning the target. Therefore, the reasonable and effective dynamic assignment of emitter reconnaissance and confrontation tasks has also become one new challenge. This paper proposes a dynamic swarm task assignment strategy to solve the above problem. For emitter reconnaissance and positioning, we study the multi-target cooperation among an UAV swarm, and take UAV relative position structure into consideration. This strategy uses the unique role conversion of the artificial bee colony algorithm to assign tasks. We also constructed a joint optimization model based on range difference and the Cramer-Rao Lower Bound with time, space and target adaptability. When the original flight trajectory does not change, the self-organizing and cooperative swarm can optimize positioning formation clustering. Simulation results show that this strategy can quickly achieve higher positioning accuracy and lower energy consumption with UAV loss rate of 8%. After \(5\) dynamic task assignments, the Root Mean Square Error is less than 50 m.KeywordsUAV swarmsEmitter positionTask assignment