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Open AccessArticle

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

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Fraunhofer IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
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Institute of Information Technology (Communication Electronics), Friedrich-Alexander University (FAU), 91058 Erlangen-Nürnberg, Germany
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Georg Simon Ohm Institute of Technology, 90489 Nürnberg, Germany
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Programming Systems Group, Friedrich-Alexander University (FAU), 91058 Erlangen-Nürnberg, Germany
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Stahlke, M.; Kram, S.; Mumme, T.; Seitz, J. Discrete Positioning Using UWB Channel Impulse Responses and Machine Learning. In Proceedings of the 2019 International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany, 4–6 June 2019.
Sensors 2019, 19(24), 5547; https://doi.org/10.3390/s19245547
Received: 31 October 2019 / Revised: 28 November 2019 / Accepted: 11 December 2019 / Published: 16 December 2019
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. View Full-Text
Keywords: Ultra-Wideband; positioning; channel modeling; machine learning; feature extraction Ultra-Wideband; positioning; channel modeling; machine learning; feature extraction
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Kram, S.; Stahlke, M.; Feigl, T.; Seitz, J.; Thielecke, J. UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis. Sensors 2019, 19, 5547.

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