Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification

WEI Zihui; XIE Yunlong; WANG Shizhao; YE Xingyue; ZHANG Yaofa; FANG Lide

doi:10.11999/JEIT210422

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2842-2851

WEI Zihui, XIE Yunlong, WANG Shizhao, YE Xingyue, ZHANG Yaofa, FANG Lide. Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2842-2851. doi: 10.11999/JEIT210422

Citation:

WEI Zihui, XIE Yunlong, WANG Shizhao, YE Xingyue, ZHANG Yaofa, FANG Lide. Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2842-2851. doi: 10.11999/JEIT210422

Citation:

WEI Zihui, XIE Yunlong, WANG Shizhao, YE Xingyue, ZHANG Yaofa, FANG Lide. Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2842-2851. doi: 10.11999/JEIT210422

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Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification

doi: 10.11999/JEIT210422 cstr: 32379.14.JEIT210422

WEI Zihui^{1, 2, 3},
XIE Yunlong¹,
WANG Shizhao¹,
YE Xingyue¹,
ZHANG Yaofa¹,
FANG Lide^{1, 2, 3
,
,}

1.
School of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2.
National & Local Joint Engineering Research Center of Metrology Instrument and System, Baoding 071002, China
3.
Baoding Industrial Metrology Engineering Technology Research Center, Baoding 071002, China

Funds: The National Natural Science Foundation of China (61475041), Beijing-Tianjin-Hebei Collaborative Innovation Community Construction Project (20540301D), The Natural Science Foundation of Hebei Province (E2017201142), The Graduate Innovation Funding Project of Hebei Province (hbu2020ss063)

Received Date: 2021-05-18
Rev Recd Date: 2021-09-11

Available Online: 2021-09-27

Publish Date: 2022-08-17

Abstract

Abstract

In the Ultra-WideBand (UWB) positioning system, the signal occlusion and the misjudgment of the direct signal affect seriously the positioning accuracy in complex environment. To solve this problem, Saturation (S) is proposed, which is a new characteristic parameter based on Channel Impulse Response (CIR). In this study, the Relief algorithm and the Mutual Information Feature Selection (MIFS) algorithm are used for feature selection combined with feature parameters proposed by researchers. Based on the correlation of the parameters, the optimal feature subset with corresponding weights is used for weighted K-nearest neighbor classification, which improves the accuracy of the Non-Line-Of-Sight (NLOS) recognition system. The influence of the number of training dataset and the value of K on the Weighted K-Nearest Neighbor (WKNN) algorithm is analyzed. An optimization scheme is proposed to reduce the amount of calculation and improve the real-time performance of the NLOS recognition system. The experimental results in different environments show that the method has high recognition accuracy and wide applicability, and the recognition accuracy reaches 95%.
- Ultra-WideBand(UWB) positioning,
- Channel Impulse Response(CIR),
- Non-Line-Of-Sight(NLOS) recognition,
- Feature selection,
- Weighted K-Nearest Neighbor(WKNN)

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References(25)

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