Wireless Sensor Networks localization with outliers and structured missing data

In this paper we address the Wireless Sensor Networks localization problem in a realistic scenario with outliers and structured missing data (i.e. non-random). Our approach couples SMACOF, which handles incomplete data, with IRLS, which is resilient to outliers. In addition, we provide a new insight on how the initialization method — which is crucial to ensure fold-free solutions — should be adapted to the pattern of missing measures. Experiments shows that the proposed method compares favorably with the state-of-the-art.