Deep Learning Soft-Decision GNSS Multipath Detection
and Mitigation
Nunes, F.
;
Sousa, F.
Sensors Vol. 24, Nº 14, pp. 1 - 20, July, 2024.
ISSN (print):
ISSN (online): 1424-8220
Scimago Journal Ranking: 0,76 (in 2022)
Digital Object Identifier: 10.3390/s24144663
Download Full text PDF ( 557 KBs)
Downloaded 1 time
Abstract
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 preprocessing 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).