Geometry-based non-line-of-sight error mitigation and localization in wireless communications

Hua, Jingyu; Yin, Yong; Wang, Anding; Lu, Weidang

doi:10.1007/s11432-019-9909-5

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…Koledoye et al [12] proposed a method for evaluating measurement noise and NLOS biases based on fixed node placement. Hua et al [13]] have introduced a new NLOS mitigation approach, where the geometric relationship among a mobile and base station was utilized. The range measurements that were damaged by the NLOS errors were identified and the position was estimated by the mobile station via the LOS measurements.…”

Section: A Related Workmentioning

confidence: 99%

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Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

Mahapatro¹,

Rayavarapu²

2021

Preprint

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<div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div>

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Section: A Related Workmentioning

confidence: 99%

“…MDS scheme [12] permits the larger node deployments, and also symmetry is exhibited that permits the re-computing of precise anchor positions. Geometry-based NLOS [13] is applicable for the real-world scenarios and also describes the detection threshold by exploiting a semianalytical method. Yet, heavy calibration is required.…”

Section: B Reviewmentioning

confidence: 99%

Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

Mahapatro¹,

Rayavarapu²

2021

Preprint

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“…The methods for improving the localization accuracy of the network mainly fall into two categories: the NLOS identification 34 algorithm and the NLOS mitigating algorithm. 35,36 The NLOS identification algorithm mainly identifies whether the distance measurement value obtained is LOS or NLOS. If the LOS measurements are more than three, the NLOS measurements will be discarded and some classical localization algorithms will be used for localization calculation.…”

Section: The Architecture Of the Proposed Algorithmmentioning

confidence: 99%

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

Lin

Luan

et al. 2020

International Journal of Distributed Sensor Networks

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As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

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“…With the development of IoT, Internet of Vehicles (IoV) is evolving as a new theme of research from traditional vehicular Adhoc networks (VANETs) [3,4]. Sensing, communication, computing and networking technologies are integrated in IoV, enabling more intelligent driving experiences such like vehicle localization, behavior analysis even automatic driving [5][6][7]. For these applications, the vehicle speed estimated by perception operation is an essential input.…”

Section: Introductionmentioning

confidence: 99%

Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system

Wen

Yan

Feng

et al. 2022

Preprint

Self Cite

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Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF(autocorrelation function)-DSR(double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking into account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.

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Geometry-based non-line-of-sight error mitigation and localization in wireless communications

Cited by 19 publications

References 34 publications

Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system

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