An Efficient UAV Localization Technique Based on Particle Swarm Optimization

Unmanned aerial vehicles (UAVs) have recently attracted tremendous attentions in both industries and academic communities. Thanks to the high mobility and flexibility, UAVs can be deployed in many scenarios to provide various types of services. In these scenarios, the position of the UAVs must be timely and accurately acquired to avoid UAV collisions and realize millimeter-wave beamforming. Particle swarm optimization (PSO) is a potential approach to fulfill localization under GPS-denied environment. However, it has the drawbacks of high complexity and relative large localization error. In this article, we consider the UAV localization problem based on improved PSO, which aims at reducing complexity and localization error. We firstly analyze the performance metrics and performance bounds of conventional PSO in the considered UAV localization scenario. Then, the particle initialization process is reconsidered, where a particle and search space reduction method is introduced as the hierarchical PSO (HPSO). Next, the particle updating schemes are redesigned based on the particle number, where the reference best particle is introduced to deal with the limitations in conventional PSO, this is called reference PSO (RPSO). Lastly, the proposed HPSO and RPSO are validated in simulation results. It is shown that the proposed PSO method has both reduced complexity and localization error compared with conventional PSO and other reference methods.