GNSS NLOS signals identification based on deep neural networks

Fang Li; Zhiqiang Dai; Tianci Li; Xiangwei Zhu

doi:10.1117/12.2640451

1 August 2022 GNSS NLOS signals identification based on deep neural networks

Fang Li, Zhiqiang Dai, Tianci Li, Xiangwei Zhu

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570O (2022) https://doi.org/10.1117/12.2640451
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Global navigation satellite system (GNSS) signals are easily blocked and reflected by surroundings in urban environments, resulting in a large number of non-line-of-sight (NLOS) signals and multipath effects, which will extremely degrade GNSS positioning precision and reliability. This paper proposes the GNSS NLOS signals identification method based on the fully connected neural networks (FCNNs) in deep neural networks (DNN), which extracts representative features from GNSS original observations, and uses the DNN to realize effectively signal classification tasks. This method improves the precision and reliability of GNSS positioning by predicting the NLOS signals accurately. The experimental results show that the Doppler measurement, C/N0, elevation angle, azimuth, and the NLOS visibility ratio can be used as representative parameters of the current NLOS identification task. Compared with the decision tree, and the support vector machine, the experimental results show that the prediction effect of the FCNNs algorithm is better than the former two algorithms.

Citation Download Citation

Fang Li, Zhiqiang Dai, Tianci Li, and Xiangwei Zhu "GNSS NLOS signals identification based on deep neural networks", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570O (1 August 2022); https://doi.org/10.1117/12.2640451

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KEYWORDS

Satellite navigation systems

Non-line-of-sight propagation

Satellites

Visibility

Doppler effect

Neural networks

Evolutionary algorithms

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