An enhanced TDoA approach handling multipath interference in Wi-Fi based indoor localization systems

Xie, Tianbao; Zhang, Chun; Li, Yongming; Jiang, Hanjun; Wang, Zhihua

doi:10.1109/mwscas.2017.8052885

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…In [12], Exel et al realized nanosecond accuracy by extracting timestamps in an IEEE 802.11b wireless local area network (WLAN) with a special synchronized receiver. In [13], an enhanced TDOA approach based on the RSS-assisted cross-correlation method was proposed to handle multipath interference in indoor environments. However, under situations in which TDOA measurements are strongly affected by multipath interference, it is not suitable for indoor localization scenarios.…”

Section: Introductionmentioning

confidence: 99%

ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments

Chen

Wang

2019

Sensors

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In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in the indoor environments, we utilize the azimuth and RSS estimation results, which are calculated by using the eigenspace-based DOA (ES-DOA) algorithm, in a novel style. A direct-path bearing recognition algorithm is introduced to identify the real DOA of the signal source in different indoor environments, by combining the azimuth and RSS estimation with ensemble learning methods. Numerical simulations are conducted to verify the validity and superiority of the proposed method. The results show that the proposed ES-DPR method can achieve high resolution and has strong anti-noise capability in dealing with the multipath signals, and the direct-path recognition algorithm is reliable and robust in different indoor environments, even in undetectable direct-path conditions.

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Section: Introductionmentioning

confidence: 99%

ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments

Chen

Wang

2019

Sensors

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“…Therefore, research on indoor 2 of 22 positioning is currently a popular topic. Indoor positioning technology provides the TOA (time difference of arrival) based on the electromagnetic wave propagation time positioning technology [5,6], the AOA (angle of arrival) based on the incidence angle of electromagnetic waves [7,8], the TDOA based on the arrival of the frequency of magnetic wave positioning technology [9,10], signal strength based on RSSI (received signal strength indication) positioning technology [11,12], and the AP (access point) sensor signal strength based on positioning technology [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

Wang

2019

IJGI

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The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.

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Cramér-Rao lower bound analysis of RSS/TDoA joint localization algorithms based on rigid graph theory

Wang

et al. 2020

Ad Hoc Networks

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An enhanced TDoA approach handling multipath interference in Wi-Fi based indoor localization systems

Cited by 8 publications

References 13 publications

ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments

ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

Cramér-Rao lower bound analysis of RSS/TDoA joint localization algorithms based on rigid graph theory

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