Distributed Interference Optimization Method of Large-scale UAV Based on Tabu Search Artificial Bee Colony Algorithm

Abstract

With the rapid development of UAV control link and ad hoc network, and the low success rate of single UAV mission, a large number of UAVs have become the mainstream in disaster relief, security and other fields. In the airport, the interference and Countermeasure of the competition venue against UAVs has become the core link of security in large-scale events and important places. The existing interference sources are mainly single point interference. Due to the shielding of buildings, it is impossible to fully cover the venue. Therefore, distributed interference optimization is the best option to combat large-scale UAVs in the future. On the basis of summarizing the jamming benefit evaluation system, this paper introduces the probability of jamming source discovery as the jamming cost, and establishes the satellite navigation distributed jamming optimization model. Tabu search artificial bee colony algorithm (TSABC) is proposed to solve the problems of slow interference speed and low success rate of traditional distributed interference optimization algorithm. The algorithm proposed in this paper is compared with ABC algorithm and PSO algorithm in interference countermeasure scenario. The simulation results show that the TSABC algorithm proposed in this paper effectively improves the interference efficiency and can quickly and accurately interfere with a large number of UAVs.