| Summary: | For positioning system based on wireless sensor networks, NLOS errors are one of the main factors to degrade localization performance of an algorithm, about which lots of research results and analysis have been published in previous literatures to enhance localization performance. However, those literatures have neglected computational time, another important index to performance. To decrease computational time and improve localization accuracy simultaneously, we firstly consider NLOS errors as outliers and transform the TOA-based localization problem into a sparse optimization one in LOS-dominating environment. Then, we introduce sparse technology into line-of-sight/none-line-of-sight (LOS/NLOS) scenarios formulating a L1-norm minimization problem, and solve it by alternating direction method of multipliers (ADMM) with a strategy of iterative adaptive. Monte Carlo simulation results show that our method has advantages of high computation speed and positioning accuracy under mixed sparse LOS/NLOS scenarios.
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