Joint Estimation and Localization in Sensor Networks
This work addresses target tracking in sensor networks where sensor poses are unknown, offering a joint solution that is incremental over existing methods.
The paper tackles collaborative tracking of dynamic targets in wireless sensor networks by developing a novel distributed linear estimator, proving mean square consistency for static targets and strong convergence for joint localization and estimation, with performance validated in simulations.
This paper addresses the problem of collaborative tracking of dynamic targets in wireless sensor networks. A novel distributed linear estimator, which is a version of a distributed Kalman filter, is derived. We prove that the filter is mean square consistent in the case of static target estimation. When large sensor networks are deployed, it is common that the sensors do not have good knowledge of their locations, which affects the target estimation procedure. Unlike most 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. The sensors are localized via a distributed Jacobi algorithm 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 target tracking tasks.