Abstract:

Received signal strength (RSS) based localization is one of the common methods used in wireless sensor networks (WSNs) localization. Due to the non-linearity and non-convexity of the objective function, the traditional maximum likelihood estimator (MLE) can converge to local optimum when applied to WSNs localization. To overcome this problem, we propose a semi-definite programming (SDP) based localization algorithm. Firstly, the Taylor-series approximation is employed to linearize the objective function. Secondly, auxiliary variables are introduced to transform the original problem into a constraint optimization problem. Finally, the semi-definite relaxation (SDR) is applied to transform this constraint optimization problem into a SDP convex optimization problem. Comparison of simulation results shows that the proposed algorithm has higher localization accuracy and is more robust than the existing algorithms.

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