A Novel Satellite Selection Algorithm Using LSTM Neural Networks for Single-Epoch Localization

Ibrahim Sbeity, Christophe Villien, Christophe Combettes, Benoît Denis, E. Veronica Belmega, Marwa Chafii

Abstract: Abstract—This work presents a new approach for detection and exclusion (or de-weighting) of pseudo-range measurements from the Global Navigation Satellite System (GNSS) in order to improve the accuracy of single-epoch positioning, which is an essential prerequisite for maintaining good navigation performance in challenging operating contexts (e.g., under Non-Line of Sight and/or multipath propagation). Beyond the usual preliminary hard decision stage, which can mainly reject obvious outliers, our approach exploits machine learning to optimize the relative contributions from all available satellites feeding the positioning solver. For this, we construct a customized matrix of pseudorange residuals that is used as an input to the proposed long-short term memory neural network (LSTM NN) architecture. The latter is trained to predict several quality indicators that roughly approximate the standard deviations of pseudo-range errors, which are further integrated in the calculation of weights. Our numerical evaluations on both synthetic and real data show that the proposed solution is able to outperform conventional weighting and signal selection strategies from the state-of-the-art, while fairly approaching optimal positioning accuracy. Index Terms—Global Navigation Satellite System, Satellite Selection, Single-epoch Positioning, Machine (Deep) Learning, Long-Short Term Memory Neural Network
Published in: 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 24 - 27, 2023
Hyatt Regency Hotel
Monterey, CA
Pages: 105 - 112
Cite this article: Sbeity, Ibrahim, Villien, Christophe, Combettes, Christophe, Denis, Benoît, Belmega, E. Veronica, Chafii, Marwa, "A Novel Satellite Selection Algorithm Using LSTM Neural Networks for Single-Epoch Localization," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 105-112. https://doi.org/10.1109/PLANS53410.2023.10140007
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