Distributed self localization of sensors with poisson deployment using extended Kalman filter

Wireless network sensors today are deployed geographically in a random manner and are subjected to disturbances caused by natural forces, thereby necessitating real time and accurate estimation of the sensors´ locations. In this paper, we consider wireless sensor self-localization problem where sensors are deployed according to a Poisson point process. We model such sensor networks under realistic assumptions about the wireless channel and measurement updates. We propose a novel distributed and sequential algorithm based on inter-node exchange of information for estimating sensor locations and analyze their performance through numerical simulations.