IEICE TRANSACTIONS on Communications
A SOM-CNN Algorithm for NLOS Signal Identification
Summary :
Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.2 pp.117-132
- Publication Date
- 2023/02/01
- Publicized
- 2022/08/01
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022EBP3045
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Ze Fu GAO
the Space Engineering University
Hai Cheng TAO
the Space Engineering University
Qin Yu ZHU
the Space Engineering University
Yi Wen JIAO
the Space Engineering University
Dong LI
the Space Engineering University
Fei Long MAO
the Space Engineering University
Chao LI
the Space Engineering University
Yi Tong SI
the Space Engineering University
Yu Xin WANG
the Space Engineering University
Keyword
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Ze Fu GAO, Hai Cheng TAO , Qin Yu ZHU, Yi Wen JIAO, Dong LI, Fei Long MAO, Chao LI, Yi Tong SI, Yu Xin WANG, "A SOM-CNN Algorithm for NLOS Signal Identification" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 2, pp. 117-132, February 2023, doi: 10.1587/transcom.2022EBP3045.
Abstract: Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3045/_p
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@ARTICLE{e106-b_2_117,
author={Ze Fu GAO, Hai Cheng TAO , Qin Yu ZHU, Yi Wen JIAO, Dong LI, Fei Long MAO, Chao LI, Yi Tong SI, Yu Xin WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A SOM-CNN Algorithm for NLOS Signal Identification},
year={2023},
volume={E106-B},
number={2},
pages={117-132},
abstract={Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.},
keywords={},
doi={10.1587/transcom.2022EBP3045},
ISSN={1745-1345},
month={February},}
Copy
TY - JOUR
TI - A SOM-CNN Algorithm for NLOS Signal Identification
T2 - IEICE TRANSACTIONS on Communications
SP - 117
EP - 132
AU - Ze Fu GAO
AU - Hai Cheng TAO
AU - Qin Yu ZHU
AU - Yi Wen JIAO
AU - Dong LI
AU - Fei Long MAO
AU - Chao LI
AU - Yi Tong SI
AU - Yu Xin WANG
PY - 2023
DO - 10.1587/transcom.2022EBP3045
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2023
AB - Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.
ER -
English
