WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912319482626048 |
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| author | Zheng, Shilian Lin, Quan Qi, Peihan Zhang, Luxin Qiu, Xinjiang Zhao, Zhijin Yang, Xiaoniu |
| author_facet | Zheng, Shilian Lin, Quan Qi, Peihan Zhang, Luxin Qiu, Xinjiang Zhao, Zhijin Yang, Xiaoniu |
| contents | Accurate and efficient positioning in complex environments is critical for applications where traditional satellite-based systems face limitations, such as indoors or urban canyons. This paper introduces WK-Pnet, an FM-based indoor positioning framework that combines wavelet packet decomposition (WPD) and knowledge distillation. WK-Pnet leverages WPD to extract rich time-frequency features from FM signals, which are then processed by a deep learning model for precise position estimation. To address computational demands, we employ knowledge distillation, transferring insights from a high-capacity model to a streamlined student model, achieving substantial reductions in complexity without sacrificing accuracy. Experimental results across diverse environments validate WK-Pnet's superior positioning accuracy and lower computational requirements, making it a viable solution for positioning in real-time resource-constraint applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation Zheng, Shilian Lin, Quan Qi, Peihan Zhang, Luxin Qiu, Xinjiang Zhao, Zhijin Yang, Xiaoniu Signal Processing Accurate and efficient positioning in complex environments is critical for applications where traditional satellite-based systems face limitations, such as indoors or urban canyons. This paper introduces WK-Pnet, an FM-based indoor positioning framework that combines wavelet packet decomposition (WPD) and knowledge distillation. WK-Pnet leverages WPD to extract rich time-frequency features from FM signals, which are then processed by a deep learning model for precise position estimation. To address computational demands, we employ knowledge distillation, transferring insights from a high-capacity model to a streamlined student model, achieving substantial reductions in complexity without sacrificing accuracy. Experimental results across diverse environments validate WK-Pnet's superior positioning accuracy and lower computational requirements, making it a viable solution for positioning in real-time resource-constraint applications. |
| title | WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.07399 |