WLS Localization Using Skipped Filter, Hampel Filter, Bootstrapping and Gaussian Mixture EM in LOS/NLOS Conditions

Park, Chee‐Hyun

doi:10.1109/access.2019.2905367

Cited by 17 publications

(23 citation statements)

References 41 publications

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“…One approach is to first identify the NLOS measurements and discard them when carrying out localization [21,23,24]. Another approach attempts to exploit the NLOS paths together with the LOS measurements to perform localization [25][26][27][28][29][30]. In the former approach, the NLOS-LOS identifications are usually based on hypothesis testing [21,23,24] and nonparametric machine learning [26][27][28].…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

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Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Yang

et al. 2020

IEEE Trans. Commun.

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Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms. Index Terms Bayesian Cramer-Rao bound (BCRB), variational Bayesian localization (VBL), mixed LOS/NLOS measurements, anchor node uncertainties, asymptotic minimum mean square error (MMSE).

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Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…As shown in Fig. 7 In the sixth experiment, the number of particles N p is set to range in [5,30]. The NLOS paths are set to be E (α 2 ) = 0.4.…”

Section: Parameter Impactsmentioning

confidence: 99%

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Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Yang

et al. 2020

IEEE Trans. Commun.

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“…If the intersection area becomes larger, the coordinates of MS will be difficult to be estimated and the corresponding positioning performance will be degraded. Additionally, note that Gaussian noise, NLOS error and layout of BSs are the most important factors that affect the size of the intersection area [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Among them, NLOS error may be the most influential factor because it can directly lead to inaccurate measurements, resulting in inaccurate localization results or even divergence .…”

Section: Introductionmentioning

confidence: 99%

Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments

Yuan

Tan

2021

Sensors

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Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy.

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“…The experimental results show that these methods have great improvement on the detection of NLOS paths. Furthermore, some research papers have been published [5]- [16], [20]- [28] to improve the localization performance and reduce the effects of NLOS errors. In [5], a fast identification of NLOS error using eigenvector (FINE) algorithm has been proposed to identify NLOS status.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Tan

2021

IEEE Access

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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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WLS Localization Using Skipped Filter, Hampel Filter, Bootstrapping and Gaussian Mixture EM in LOS/NLOS Conditions

Cited by 17 publications

References 41 publications

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments

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

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