A Novel Ultra-Wideband-Based Localization and Tracking Scheme with Channel Classification

Liu, Zhongling; Yang, Ming; Xu, Chaojie; Yu, Hui

doi:10.1109/vtcspring.2017.8108341

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…However also a decision tree classifier has been used [12]. In contrast to most NLOS identification approaches that only consider two classes (LOS/NLOS) there is also work that considers a more fine-grained categorization [13], [14].…”

Section: Related Work a Anomaly Identificationmentioning

confidence: 99%

Estimating TOA Reliability With Variational Autoencoders

et al. 2022

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Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%.

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Section: Related Work a Anomaly Identificationmentioning

confidence: 99%

Estimating TOA Reliability With Variational Autoencoders

et al. 2022

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“…In recent years, more and more machine-and deep-learning (DL) methods have been applied to positioning problems with various sensor signals [13][14][15]. Feature-based ML and DL approaches have both been used to identify LOS/NLOS and other propagation conditions for UWB positioning system [16][17][18][19][20][21]. CIRs have been used to estimate errors and signal quality and to enhance classic tracking techniques such as Bayesian filters [22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Kram

Stahlke

Feigl

et al. 2019

Sensors

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Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

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“…Reference [7] shows that antenna beamforming technology can effectively compensate for propagation loss. Besides, dynamic beam search and tracking algorithms have also been introduced in [8,9] to reduce NLOS errors. References [10,11] use relay nodes to maintain connectivity and deploy multiple relay nodes to form a multi-hop mesh link structure.…”

Section: Introductionmentioning

confidence: 99%

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

Fan

et al. 2022

IET Communications

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Line‐of‐sight (LOS)/non‐line‐of‐sight (NLOS) identification is crucial for millimetre‐wave (mmW) since it is vulnerable to blocking in the NLOS environment. In this paper, an LOS/NLOS environment identification method is proposed based on angle information learning. Specifically, a neural network named ALNN (angle information learning neural network) is employed, which can learn the difference between the various angle paraments to identify the LOS and NLOS environments for mmW wireless communication systems. First, the 3D mmW channel model is applied to extract the desired angle information. Moreover, an mmW wireless transmission system is constructed to introduce actual measurement data to test the proposed method. Furthermore, various hyperparameters of the ALNN are adjusted to improve identification accuracy. Simulation results show that the proposed method has outstanding identification performance, and the identification accuracy is up to 99.41%.

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A Novel Ultra-Wideband-Based Localization and Tracking Scheme with Channel Classification

Cited by 6 publications

References 11 publications

Estimating TOA Reliability With Variational Autoencoders

Estimating TOA Reliability With Variational Autoencoders

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

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