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.
@article{arxiv.2504.07399,
title = {WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation},
author = {Shilian Zheng and Quan Lin and Peihan Qi and Luxin Zhang and Xinjiang Qiu and Zhijin Zhao and Xiaoniu Yang},
journal= {arXiv preprint arXiv:2504.07399},
year = {2025}
}