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

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Abstract

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 state-of-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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Acknowledgements

The authors are very much thankful to Prof. Henk Wymseersch and Dr. Tian Shiwe for providing dataset-1, which helped us to validate the classifiers.

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  1. VSSUT, Burla, India

    V Ch Sekhar Rao Rayavarapu & Arunanshu Mahapatro

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  1. V Ch Sekhar Rao Rayavarapu
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  2. Arunanshu Mahapatro
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Correspondence to Arunanshu Mahapatro.

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Rayavarapu, V.C.S.R., Mahapatro, A. NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers. Ann. Telecommun. 77, 267–280 (2022). https://doi.org/10.1007/s12243-021-00884-6

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  • DOI: https://doi.org/10.1007/s12243-021-00884-6

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