Joint estimation and localization in sensor networks

This paper addresses the problem of collaborative estimation and tracking of dynamic phenomena via a wireless sensor network. A distributed linear estimator (i.e., a type of a distributed Kalman filter) is derived. We prove that the filter is mean square consistent when estimating static phenomena. In large sensor networks, it is common that the sensors do not have good knowledge of their locations, which affects the estimation procedure. Unlike existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. A distributed Jacobi algorithm is used to localize the sensors from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and vehicle tracking tasks.