DS-Pnet: FM-Based Positioning via Downsampling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913787340128256 |
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| author | Zheng, Shilian Qiu, Xinjiang Zhang, Luxin Lin, Quan Zhao, Zhijin Yang, Xiaoniu |
| author_facet | Zheng, Shilian Qiu, Xinjiang Zhang, Luxin Lin, Quan Zhao, Zhijin Yang, Xiaoniu |
| contents | In this paper we present DS-Pnet, a novel framework for FM signal-based positioning that addresses the challenges of high computational complexity and limited deployment in resource-constrained environments. Two downsampling methods-IQ signal downsampling and time-frequency representation downsampling-are proposed to reduce data dimensionality while preserving critical positioning features. By integrating with the lightweight MobileViT-XS neural network, the framework achieves high positioning accuracy with significantly reduced computational demands. Experiments on real-world FM signal datasets demonstrate that DS-Pnet achieves superior performance in both indoor and outdoor scenarios, with space and time complexity reductions of approximately 87% and 99.5%, respectively, compared to an existing method, FM-Pnet. Despite the high compression, DS-Pnet maintains robust positioning accuracy, offering an optimal balance between efficiency and precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DS-Pnet: FM-Based Positioning via Downsampling Zheng, Shilian Qiu, Xinjiang Zhang, Luxin Lin, Quan Zhao, Zhijin Yang, Xiaoniu Signal Processing In this paper we present DS-Pnet, a novel framework for FM signal-based positioning that addresses the challenges of high computational complexity and limited deployment in resource-constrained environments. Two downsampling methods-IQ signal downsampling and time-frequency representation downsampling-are proposed to reduce data dimensionality while preserving critical positioning features. By integrating with the lightweight MobileViT-XS neural network, the framework achieves high positioning accuracy with significantly reduced computational demands. Experiments on real-world FM signal datasets demonstrate that DS-Pnet achieves superior performance in both indoor and outdoor scenarios, with space and time complexity reductions of approximately 87% and 99.5%, respectively, compared to an existing method, FM-Pnet. Despite the high compression, DS-Pnet maintains robust positioning accuracy, offering an optimal balance between efficiency and precision. |
| title | DS-Pnet: FM-Based Positioning via Downsampling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.07429 |