Abstract
Frequency modulation (FM) broadcast signals, as opportunity 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 an end-to-end FM-based
positioning method that leverages deep learning, known as FM-Pnet. This
method utilizes the time-frequency representation of FM signals as the
network input, allowing the network to automatically learn deep features
for positioning. We further 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.