A Semidefinite Programming Algorithm for Improving Noisy Sensor Positions Using Accurate Inter-Sensor Range Measurements

In wireless sensor networks (WSNs), node localization issue from pairwise Euclidean distance measurements has become a fundamental research topic. In this paper, we investigate the issue of improving noisy sensor positions using accurate connected inter-sensor range measurements. Different from the previous researchers proposed an efficient algorithm for the case of fully connected, a semidefinite programming (SDP) method is derived to relax the nonconvex maximum likelihood estimation (MLE) problem into convex problem. To be specific, a semidefinite programming method for improving noisy sensor positions using accurate inter-sensor range measurements is proposed and it can be applied to both fully and partially connected case simultaneously. Finally, numerical simulations are conducted to demonstrate the performance of the proposed algorithm by comparing with the Cramér-Rao lower bound (CRLB), which illustrates the effects of the proposed algorithm.

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