Improved localization by time of arrival for Internet of Things in 3D

The demand of smart objects in our life promotes an advanced generation of communications under the umbrella of Internet of Things (IoT). In IoT, location-based service is one of the most promising services, where localization accuracy is a crucial problem. Rather than linearizing a nonlinear cost function used in ordinary localization problem, we propose a novel subspace algorithm by using the time of arrival (TOA) measurements. Our algorithm provides a closed-form solution and is validated to be robust for large measurement noise, as the dimension awareness and eigen structure of scalar product matrix are used. Moreover, we develop a Cramér-Rao Lower Bound (CLB) for 3-dimensional (3D) localization by using the TOA measurements. We evaluate the proposed algorithm by comparing with CLB and show its performance through simulation experiments.