Alternating Direction Method of Multipliers for TOA-Based Positioning Under Mixed Sparse LOS/NLOS Environments

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.

Automated summary

This study considers NLOS errors as outliers and transforms the TOA-based localization problem into a sparse optimization one in LOS-dominating environment. Sparse technology is introduced into LOS/NLOS scenarios, formulating a L1-norm minimization problem, and solved using ADMM algorithm with iterative adaptive strategy. The method demonstrates advantages of high computation speed and positioning accuracy under mixed sparse LOS/NLOS scenarios based on Monte Carlo simulation results.