UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models
This work addresses localization for ground users in UAV-aided wireless networks, presenting an incremental improvement by integrating neural networks into channel modeling.
The paper tackles ground user localization using RSS measurements from a UAV by proposing a hybrid channel model that combines a path loss model with a neural network to estimate unknown parameters, achieving accurate localization through PSO with simulations and real-world experiments.
This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user link channel model parameters and antenna radiation pattern of the UAV as unknowns that need to be estimated. A hybrid channel model is proposed that consists of a traditional path loss model combined with a neural network approximating the UAV antenna gain function. With this model and a set of offline RSS measurements, the unknown parameters are estimated. We then employ the particle swarm optimization (PSO) technique which utilizes the learned hybrid channel model along with a 3D map of the environment to accurately localize the ground users. The performance of the developed algorithm is evaluated through simulations and also real-world experiments.