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arXiv · 2504.07429

DS-Pnet: FM-Based Positioning via Downsampling

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

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.

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Shilian Zheng, Xinjiang Qiu, Luxin Zhang, Quan Lin, Zhijin Zhao, Xiaoniu Yang. 2025-04-10. DS-Pnet: FM-Based Positioning via Downsampling. https://arxiv.org/abs/2504.07429

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