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In this paper, the relative location estimation problem, a prominent issue faced by several applications in wireless sensor networks (WSNs), is considered. Sensors are classified into two categories: location-aware and location-unaware sensors. To estimate the positions of location-unaware sensors, exact positions are often assumed for location-aware sensors. However, in practice, such precise data may not be available. Therefore, determining the positions of location-unaware sensors in the presence of inexact positions of location-aware sensors is the primary focus of this study. A robust min-max optimization method is proposed for the relative location estimation problem by minimizing the worst-case estimation error. The corresponding optimization problem is originally nonconvex, but after it is transformed into a convex semidefinite program (SDP), it can be solved by existing numerical techniques. In the presence of inexact positions of location-aware sensors, the robustness of the proposed approach is validated by simulations under different WSN topologies. Modified maximum-likelihood (ML) estimation and second-order cone programming (SOCP) relaxation methods have been used for localization in comparison with the proposed approach.