Distributed Estimation and Tracking Using Time-Difference-of-Arrival (TDOA) Measurements

Localization and tracking of a moving target arises in many different contexts and is an important problem in the field of wireless sensor networks. One class of localization schemes exploits the time-difference-of-arrival (TDOA) of a signal emitted by the target and detected by multiple sensors. Much of the existing work on TDOA-based target localization and tracking adopts a centralized approach, where all measurements are sent to a reference agent which produces an estimate of the target’s location. In this work, we propose a fully distributed approach to target localization and tracking by a group of mobile robots. Specifically, we utilize a Networked Extended Kalman Filter (NEKF) to estimate the target’s location in a distributed manner. The target location estimates by individual robots, which are shown to converge to the true value, are then fed into a distributed control law that maintains a specified formation of the robots around the target, which optimizes the estimation accuracy. In order to reduce the energy expenditure of the robots, we further propose a movement control strategy based on the Cramer-Rao bound to balance the trade-off between estimation performance and the total distance traveled by the robots. A numerical example involving robots with unicycle dynamics is provided to illustrate the performance of the proposed approach.