Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability
With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/4/1211 |
| _version_ | 1817987259081687040 |
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| author | Ke Han Lingjie Shi Zhongliang Deng Xiao Fu Yun Liu |
| author_facet | Ke Han Lingjie Shi Zhongliang Deng Xiao Fu Yun Liu |
| author_sort | Ke Han |
| collection | DOAJ |
| description | With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively. |
| first_indexed | 2024-04-14T00:19:42Z |
| format | Article |
| id | doaj.art-1b8a417455df4965ba81a51de56b4b3e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:19:42Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1b8a417455df4965ba81a51de56b4b3e2022-12-22T02:23:00ZengMDPI AGSensors1424-82202020-02-01204121110.3390/s20041211s20041211Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion AdaptabilityKe Han0Lingjie Shi1Zhongliang Deng2Xiao Fu3Yun Liu4School of Electronic Engineering, Beijing University of Posts and Telecommunications, No.10 XiTuCheng Road, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, No.10 XiTuCheng Road, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, No.10 XiTuCheng Road, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, No.10 XiTuCheng Road, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, No.10 XiTuCheng Road, Beijing 100876, ChinaWith the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively.https://www.mdpi.com/1424-8220/20/4/1211channel state informationindoor positioningintrusion detectionnon-line-of-sight |
| spellingShingle | Ke Han Lingjie Shi Zhongliang Deng Xiao Fu Yun Liu Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability Sensors channel state information indoor positioning intrusion detection non-line-of-sight |
| title | Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability |
| title_full | Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability |
| title_fullStr | Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability |
| title_full_unstemmed | Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability |
| title_short | Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability |
| title_sort | indoor nlos positioning system based on enhanced csi feature with intrusion adaptability |
| topic | channel state information indoor positioning intrusion detection non-line-of-sight |
| url | https://www.mdpi.com/1424-8220/20/4/1211 |
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