.png){kind=icon}
.png){kind=icon}
Return
FM-Based Positioning via Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1109/JSAC.2024.3413961.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Frequency modulation
Deep learning
Time-frequency analysis
Probabilistic logic
Wireless fidelity
Fingerprint recognition
Accuracy
FM signal
positioning
deep learning
convolutional neural network
Journal
IF:
17.2
Papers:
6.4K
Citations:
3.1W