Unmanned Aerial Vehicles Path Planning Algorithm for Angle of Arrival Localization

In this paper, the problem of Unmanned Aerial Vehicles (UAVs) path planning aiming at improving the accuracy of AOA localization is investigated. The actual noise variance of the AOA measurement is set to be dependent on distance. To begin, the target's position estimation is gathered by the maximum likelihood estimation (MLE) algorithm and the extended Kalman filter (EKF) algorithm. Then, the path planning model is established with the objective function of maximizing the trace of Cramer-Rao Low Bound (CRLB), considering various practical constraints. The nonlinear constraint problem is solved with the help of the penalty function and the particle swarm optimization (PSO) algorithm. The proposed algorithm is effective, as demonstrated by the simulation results.