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.
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