Frequency Modulation (FM) broadcast signals, regarded as opportunistic signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on Received Signal Strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce FM-Pnet, an end-to-end FM-based positioning method that leverages deep learning. This method utilizes the time-frequency representation of FM signals as network input, enabling automatically learning of deep features for positioning. We also propose two strategies, noise injection and enriching training samples, to enhance the model’s generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.

中文翻译：

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通过深度学习进行基于 FM 的定位


调频 (FM) 广播信号被视为机会信号，在室内和室外定位应用中具有巨大潜力。现有的基于FM的定位方法主要依靠接收信号强度(RSS)进行定位，其精度有待提高。在本文中，我们介绍了 FM-Pnet，一种利用深度学习的基于端到端 FM 的定位方法。该方法利用 FM 信号的时频表示作为网络输入，能够自动学习定位的深层特征。我们还提出了两种策略：噪声注入和丰富训练样本，以增强模型在长时间跨度内的泛化性能。我们为室内和室外场景构建数据集，并进行大量实验来验证我们提出的方法的性能。实验结果表明，FM-Pnet在定位精度和稳定性方面均明显优于传统的基于RSS的定位方法。