The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
120
Dengan ketibaan 5G dan populariti peranti pintar, kebolehlaksanaan teknikal penyetempatan dalaman telah disahkan, dan permintaan pasarannya sangat besar. Maklumat keadaan saluran (CSI) yang diekstrak daripada Wi-Fi ialah maklumat lapisan fizikal yang lebih halus daripada petunjuk kekuatan isyarat yang diterima (RSSI). Makalah ini mencadangkan algoritma penyetempatan pembetulan CSI menggunakan DenseNet, yang dipanggil CorFi. Kaedah ini mula-mula menggunakan hutan pengasingan untuk menghapuskan CSI yang tidak normal, dan kemudian membina cap jari amplitud CSI yang mengandungi maklumat masa, kekerapan dan pasangan antena. Dalam peringkat luar talian, rangkaian konvolusi yang bersambung padat (DenseNet) dilatih untuk mewujudkan surat-menyurat antara CSI dan kedudukan spatial, dan interpolasi lanjutan umum digunakan untuk membina pangkalan data cap jari terinterpolasi. Dalam peringkat dalam talian, DenseNet digunakan untuk anggaran kedudukan, dan pangkalan data cap jari interpolasi dan jiran terdekat K (KNN) digabungkan untuk membetulkan kedudukan keputusan ramalan dengan kebarangkalian maksimum yang rendah. Dalam persekitaran koridor dalaman, ralat penyetempatan purata ialah 0.536m.
Junna SHANG
Hangzhou Dianzi University
Ziyang YAO
Hangzhou Dianzi University
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Salinan
Junna SHANG, Ziyang YAO, "Study in CSI Correction Localization Algorithm with DenseNet" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 1, pp. 76-84, January 2022, doi: 10.1587/transcom.2021EBP3033.
Abstract: With the arrival of 5G and the popularity of smart devices, indoor localization technical feasibility has been verified, and its market demands is huge. The channel state information (CSI) extracted from Wi-Fi is physical layer information which is more fine-grained than the received signal strength indication (RSSI). This paper proposes a CSI correction localization algorithm using DenseNet, which is termed CorFi. This method first uses isolation forest to eliminate abnormal CSI, and then constructs a CSI amplitude fingerprint containing time, frequency and antenna pair information. In an offline stage, the densely connected convolutional networks (DenseNet) are trained to establish correspondence between CSI and spatial position, and generalized extended interpolation is applied to construct the interpolated fingerprint database. In an online stage, DenseNet is used for position estimation, and the interpolated fingerprint database and K-nearest neighbor (KNN) are combined to correct the position of the prediction results with low maximum probability. In an indoor corridor environment, the average localization error is 0.536m.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3033/_p
Salinan
@ARTICLE{e105-b_1_76,
author={Junna SHANG, Ziyang YAO, },
journal={IEICE TRANSACTIONS on Communications},
title={Study in CSI Correction Localization Algorithm with DenseNet},
year={2022},
volume={E105-B},
number={1},
pages={76-84},
abstract={With the arrival of 5G and the popularity of smart devices, indoor localization technical feasibility has been verified, and its market demands is huge. The channel state information (CSI) extracted from Wi-Fi is physical layer information which is more fine-grained than the received signal strength indication (RSSI). This paper proposes a CSI correction localization algorithm using DenseNet, which is termed CorFi. This method first uses isolation forest to eliminate abnormal CSI, and then constructs a CSI amplitude fingerprint containing time, frequency and antenna pair information. In an offline stage, the densely connected convolutional networks (DenseNet) are trained to establish correspondence between CSI and spatial position, and generalized extended interpolation is applied to construct the interpolated fingerprint database. In an online stage, DenseNet is used for position estimation, and the interpolated fingerprint database and K-nearest neighbor (KNN) are combined to correct the position of the prediction results with low maximum probability. In an indoor corridor environment, the average localization error is 0.536m.},
keywords={},
doi={10.1587/transcom.2021EBP3033},
ISSN={1745-1345},
month={January},}
Salinan
TY - JOUR
TI - Study in CSI Correction Localization Algorithm with DenseNet
T2 - IEICE TRANSACTIONS on Communications
SP - 76
EP - 84
AU - Junna SHANG
AU - Ziyang YAO
PY - 2022
DO - 10.1587/transcom.2021EBP3033
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2022
AB - With the arrival of 5G and the popularity of smart devices, indoor localization technical feasibility has been verified, and its market demands is huge. The channel state information (CSI) extracted from Wi-Fi is physical layer information which is more fine-grained than the received signal strength indication (RSSI). This paper proposes a CSI correction localization algorithm using DenseNet, which is termed CorFi. This method first uses isolation forest to eliminate abnormal CSI, and then constructs a CSI amplitude fingerprint containing time, frequency and antenna pair information. In an offline stage, the densely connected convolutional networks (DenseNet) are trained to establish correspondence between CSI and spatial position, and generalized extended interpolation is applied to construct the interpolated fingerprint database. In an online stage, DenseNet is used for position estimation, and the interpolated fingerprint database and K-nearest neighbor (KNN) are combined to correct the position of the prediction results with low maximum probability. In an indoor corridor environment, the average localization error is 0.536m.
ER -