SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning
Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabil...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/8/11/1323 |
| _version_ | 1811186423259201536 |
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| author | Donald L. Hall Ram M. Narayanan David M. Jenkins |
| author_facet | Donald L. Hall Ram M. Narayanan David M. Jenkins |
| author_sort | Donald L. Hall |
| collection | DOAJ |
| description | Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic <i>k</i>-nearest-neighbor (P<i>k</i>NN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation. |
| first_indexed | 2024-04-11T13:46:13Z |
| format | Article |
| id | doaj.art-76f410cd94ca4695bfcf3570afaf4590 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T13:46:13Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-76f410cd94ca4695bfcf3570afaf45902022-12-22T04:21:05ZengMDPI AGElectronics2079-92922019-11-01811132310.3390/electronics8111323electronics8111323SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep LearningDonald L. Hall0Ram M. Narayanan1David M. Jenkins2Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USAApplied Research Laboratory, Pennsylvania State University, University Park, PA 16802, USAWireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic <i>k</i>-nearest-neighbor (P<i>k</i>NN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.https://www.mdpi.com/2079-9292/8/11/1323beaconingdeep learningdenoising autoencoderindoor positioningmultipath channel estimationpolarization diversityvector sensor |
| spellingShingle | Donald L. Hall Ram M. Narayanan David M. Jenkins SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning Electronics beaconing deep learning denoising autoencoder indoor positioning multipath channel estimation polarization diversity vector sensor |
| title | SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning |
| title_full | SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning |
| title_fullStr | SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning |
| title_full_unstemmed | SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning |
| title_short | SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning |
| title_sort | sdr based indoor beacon localization using 3d probabilistic multipath exploitation and deep learning |
| topic | beaconing deep learning denoising autoencoder indoor positioning multipath channel estimation polarization diversity vector sensor |
| url | https://www.mdpi.com/2079-9292/8/11/1323 |
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