Multi-Sources Information Fusion Learning for Multi-Points NLOS Localization
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912131327197184 |
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| author | Wang, Bohao Zhu, Fenghao Liu, Mengbing Huang, Chongwen Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Debbah, Mérouane |
| author_facet | Wang, Bohao Zhu, Fenghao Liu, Mengbing Huang, Chongwen Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Debbah, Mérouane |
| contents | Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from channel information in pre-defined rectangular areas, struggle with the heterogeneous fingerprint distribution inherent in non-line-of-sight (NLOS) scenarios. To address the problem, we introduce a novel multi-source information fusion learning framework referred to as the Autosync Multi-Domain NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform fingerprint distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results demonstrate that AMDNLoc significantly enhances localization accuracy by over 40\% compared with traditional convolutional neural networks on the wireless artificial intelligence research dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Sources Information Fusion Learning for Multi-Points NLOS Localization Wang, Bohao Zhu, Fenghao Liu, Mengbing Huang, Chongwen Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Debbah, Mérouane Information Theory Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from channel information in pre-defined rectangular areas, struggle with the heterogeneous fingerprint distribution inherent in non-line-of-sight (NLOS) scenarios. To address the problem, we introduce a novel multi-source information fusion learning framework referred to as the Autosync Multi-Domain NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform fingerprint distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results demonstrate that AMDNLoc significantly enhances localization accuracy by over 40\% compared with traditional convolutional neural networks on the wireless artificial intelligence research dataset. |
| title | Multi-Sources Information Fusion Learning for Multi-Points NLOS Localization |
| topic | Information Theory |
| url | https://arxiv.org/abs/2401.12538 |