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Article

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

1
Future Robotics Technology Center, Chiba Institute of Technology, Chiba 2750016, Japan
2
Department of Applied Mechanics and Aerospace Engineering, Waseda University, Tokyo 1620044, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Jaume Sanz Subirana
Sensors 2021, 21(7), 2503; https://doi.org/10.3390/s21072503
Received: 26 February 2021 / Revised: 25 March 2021 / Accepted: 31 March 2021 / Published: 3 April 2021
(This article belongs to the Special Issue GNSS Data Processing and Navigation in Challenging Environments)
This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated. View Full-Text
Keywords: GPS; GNSS; SDR; multipath; signal classification; machine learning GPS; GNSS; SDR; multipath; signal classification; machine learning
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MDPI and ACS Style

Suzuki, T.; Amano, Y. NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning. Sensors 2021, 21, 2503. https://doi.org/10.3390/s21072503

AMA Style

Suzuki T, Amano Y. NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning. Sensors. 2021; 21(7):2503. https://doi.org/10.3390/s21072503

Chicago/Turabian Style

Suzuki, Taro, and Yoshiharu Amano. 2021. "NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning" Sensors 21, no. 7: 2503. https://doi.org/10.3390/s21072503

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