Measurement and analysis of NLOS identification metrics for WLAN systems

Almazrouei, Ebtesam; Sindi, Nayef Al; Al-Araji, Saleh R.; Ali, Nazar; Chaloupka, Zdenek; Aweya, James

doi:10.1109/pimrc.2014.7136175

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Although the generalized likelihood ratio test algorithm is relatively simple to implement and can be adapted to a variety of signal models and statistical distributions, it is difficult to adjust and optimize it for uncertain or unknown signal distributions that can produce false positives and false alarms or which missed alarms in some specific cases. If the prior probability of LOS/NLOS propagation is unknown, NLOS propagation can be identified by testing whether the measurements obey a Gaussian distribution [28][29][30]. In recent years, tests such as K-S, A-D, chi-Square, gross test, skewness and cliffness tests have emerged [31][32][33].…”

Section: Root-mean-square Time Delay Extension Methods For Comparing ...mentioning

confidence: 99%

Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning

2023

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Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning.

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Section: Root-mean-square Time Delay Extension Methods For Comparing ...mentioning

confidence: 99%

Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning

2023

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“…We define the NLOS scenario when the TOA error is greater than 0 Ts. In OFDM systems, some NLOS identification technologies [16,17] have already performed well. In this article we assume that the recognition result of NLOS is prior information, and let LOS status = 0, 1 where 1 indicates NLOS and 0 indicates LOS.…”

Section: Toa Error Distributionmentioning

confidence: 99%

Deep Learning based OTDOA Positioning for NB-IoT Communication Systems

Pan

Wang

Jiang³

et al. 2020

Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cy

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“…Note that NLOS state recognition algorithms based on distance estimation [ 7 , 8 , 9 ] and channel impulse response (CIR) features of UWB [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ] are most commonly used to identify NLOS propagation state. However, compared with the CIR feature method, the distance estimation will cause extra delay due to the collection and calculation of distance.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment

Kong

Chen

et al. 2020

Sensors

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The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.

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Measurement and analysis of NLOS identification metrics for WLAN systems

Cited by 7 publications

References 9 publications

Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning

Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning

Deep Learning based OTDOA Positioning for NB-IoT Communication Systems

Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment

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