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arXiv · 2504.07399

WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation

Shilian Zheng·Quan Lin·Peihan Qi·Luxin Zhang·Xinjiang Qiu·Zhijin Zhao·Xiaoniu Yang
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Abstract

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.

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Shilian Zheng, Quan Lin, Peihan Qi, Luxin Zhang, Xinjiang Qiu, Zhijin Zhao, Xiaoniu Yang. 2025-04-10. WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation. https://arxiv.org/abs/2504.07399

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