Abstract
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.
Highlights
Global navigation satellite systems (GNSS) have been able to provide high-precision location information services to users all over the world
NUMERICAL RESULTS four examples are given to test the localization performance and calculational speed of the proposed method (i.e., alternating direction method of multipliers (ADMM)), and it is compared with semidefinite relaxation (SDR) method [8], second-order cone relaxation (SOCR) method [8], CWLS method [33], Huber-norm method [34] and two new RSDP-new method (i.e., RSDP-New1 and RSDP-New2) by adding white Gaussian noise with variance 0.05 and 0.15 to the prior information of NLOS bias and noise variance, respectively
The positioning performance of all algorithms can be evaluated through the root mean square error (RMSE), defined by RMSE =
Summary
Global navigation satellite systems (GNSS) have been able to provide high-precision location information services to users all over the world. C. He et al.: ADMM for TOA-Based Positioning Under Mixed Sparse LOS/NLOS Environments published [5]–[16], [20]–[28] to improve the localization performance and reduce the effects of NLOS errors.
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