PROJECT TITLE :

Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster

ABSTRACT:

Passive localization of multiple UAVs based on received signal strength (RSS) is extremely important for a wide variety of applications, including search and rescue and combat on the battlefield. However, the amount of energy that is used by UAVs is a significant concern in this application that is enabled by UAVs. In most cases, the communication overhead has a significant impact on the amount of energy that is consumed. We design two distributed methods for this multi-UAV system in order to solve this problem. Both of these methods have significant performance while maintaining a low communication overhead. In the first place, a method known as distributed majorize-minimization (DMM) is suggested. In order to hasten the process of convergent convergence, a stringent upper bound of the objective function is derived from the primary one. In addition, a distributed estimation scheme that makes use of the Fisher information matrix (DEF) is presented. This scheme only calls for one round of communication to take place between the edge UAVs and the central UAV. The results of the simulation show that the proposed DMM performs better than the existing distributed iterative methods in terms of root mean square error (RMSE) while having a lower communication overhead. Furthermore, the most communication-effective DEF with local search estimation performs significantly better than the proposed DMM in terms of RMSE, despite having a higher computational complexity. This is due to the fact that it utilizes a search estimation that is performed locally.