LOS/NLOS Identification for Indoor UWB Positioning Based on Morlet Wavelet Transform and Convolutional Neural Networks

Cui, Zhichao; Gao, Yufang; Hu, Jing; Tian, Shengfeng; Cheng, Jianwei

doi:10.1109/lcomm.2020.3039251

Cited by 46 publications

(12 citation statements)

References 15 publications

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“…The CNN-based indoor positioning system is widely applied to extract features of Wi-Fi DB or RSSI, particularly for data learning model generation in preprocessing, such as in the fingerprint [ 36 , 37 , 38 ] and TOF [ 39 , 40 ] methods.…”

Section: Related Workmentioning

confidence: 99%

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High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Seong

Lee

Kim

et al. 2021

Sensors

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Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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Section: Related Workmentioning

confidence: 99%

“…On the other hand, the recurrent neural network (RNN), which is capable of time series processing owing to its repetitive structure, outputs results depending on not only the current input but also historical data. Therefore, RNNs can learn long-term dependencies [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Seong

Lee

Kim

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…As UWB signals possess large bandwidths, the identification/classification can be based on the signal properties [13]. Some of these approaches are hypothesis testing [4,14], filter-based [16], feature-based (UWB signal) classifiers (machine learning (ML) approaches) [17][18][19][20] and deep learning-based [21,22]. Cooperative localization in NLOS environments is investigated in [23,24].…”

Section: Identification and Mitigation In Uwbmentioning

confidence: 99%

NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers

Rayavarapu

Mahapatro

2021

Ann. Telecommun.

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The identification and mitigation of nonline-of-sight (NLOS) paths play crucial roles in effectively localizing sensor nodes deployed in both indoor and urban outdoor environments. In this work, the NLOS identification and mitigation tasks are performed using the bagging-based ensembled classifier. The proposed method is compared to other stateof-the-art approaches, and promising results, such as higher classification accuracy, are obtained. The received signal waveform is used as raw data, and further statistical features are extracted from the channel impulse response (CIR). The classification performances are analyzed over varying feature subsets, and associated hyperparameters are validated on three datasets. The obtained results strongly suggest implementing the bagging classifier with a proper selection of features and hyperparameters.

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“…Cui combined the Morlet wavelet transform with a CNN neural network algorithm to extract the observed values with a non-horizon error. The experimental results showed that the algorithm is more accurate [ 12 ]. PDR positioning technology uses acceleration sensors and gyroscopes to detect the step frequency, estimate the step length, and calculate the heading, and then to calculate pedestrian positions; it offers high positioning continuity and high positioning accuracy over a short time period [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint

Yuan

Zhang

Jian

et al. 2021

Sensors

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At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios.

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LOS/NLOS Identification for Indoor UWB Positioning Based on Morlet Wavelet Transform and Convolutional Neural Networks

Cited by 46 publications

References 15 publications

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers

Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint

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