Deep learning soft-decision GNSS multipath detection and mitigation

Nunes, Fernando; Sousa, Fernando

http://hdl.handle.net/10400.21/22131

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Authors

Nunes, Fernando

Sousa, Fernando

Abstract(s)

A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of 𝐶/𝑁0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision).

Keywords

Multipath detection Multipath mitigation Deep learning Convolutional neural network Multilayer perceptron

URI

http://hdl.handle.net/10400.21/22131

Citation

Nunes, F., & Sousa, F. (2024). Deep learning soft-decision GNSS multipath detection and mitigation. Sensors, 24(14), 1-20. https://doi.org/10.3390/s24144663