Transformer encoder layers optimization of single Beidou multi-frequency signal intelligent acquisition algorithm based on deep learning and its performance improvement in power positioning module

Ying He, Gong Chen, Wenqi Linghu, Jin Li, Rui Ye,
Transformer encoder layers optimization of single Beidou multi-frequency signal intelligent acquisition algorithm based on deep learning and its performance improvement in power positioning module,
Microchemical Journal,
Volume 218,
2025,
115405,
ISSN 0026-265X,
https://doi.org/10.1016/j.microc.2025.115405.
(https://www.sciencedirect.com/science/article/pii/S0026265X25027535)
Abstract: In power system implementation scenarios, due to the coexistence of complex electromagnetic environments, multipath effects, and dynamic interference, traditional rule-based Beidou signal processing methods often struggle to balance real-time performance and robustness. This paper proposes a multi-frequency feature extraction model driven by fusion perception. For multi-frequency band (such as B1, B2, B3) signals, time-domain and frequency-domain features are first extracted in parallel through a multi-channel convolutional network (1D-CNN), and then a Transformer is introduced. The bidirectional LSTM structure enables cross-frequency context awareness and deep fusion, combining the time-frequency distribution characteristics of the signal to construct a unified semantic representation. The model's output can be utilised not only for high-precision positioning calculations but also for real-time classification and anomaly detection. The modular design not only simplifies the integration process of an embedded system but also enhances the flexibility of subsequent function expansion and algorithm replacement. The experimental results show that on the standard power positioning test platform, the average positioning error is reduced to 0.48 m, which is 23 % less than that of the traditional method. The positioning accuracy rate reaches 98.7 %, which is 12.4 % higher than the baseline algorithm, fully verifying the model's efficiency and stability in complex environments
Keywords: Deep learning; Multi-frequency signal processing; Feature extraction; Intelligent localization; Performance optimization