  Recently, passive unmanned aerial vehicle (UAV) localization has become
popular due to mobility and convenience. In this paper, we consider a scenario
of using distributed drone cluster to estimate the position of a passive
emitter via received signal strength (RSS). First, a distributed
majorizeminimization (DMM) RSS-based localization method is proposed. To
accelerate its convergence, a tight upper bound of the objective function from
the primary one is derived. Furthermore, to reduce communication overhead, a
distributed estimation scheme using the Fisher information matrix (DEF) is
presented, with only requiring one-round communication between edge UAVs and
center UAV. Additionally, a local search solution is used as the initial value
of DEF. Simulation results show that the proposed DMM performs better than the
existing distributed Gauss-Newton method (DGN) in terms of root of mean square
error (RMSE) under a limited low communication overhead constraint. Moreover,
the proposed DEF performs much better than MM in terms of RMSE, but has a
higher computational complexity than the latter.
