NLOS multipath detection by using machine learning in urban environments

Taro Suzuki, Yusuke Nakano, Yoshiharu Aman

研究成果: Conference contribution

2 引用 (Scopus)

抄録

In global navigation satellite system (GNSS) positioning, GNSS satellites are often obstructed by buildings, leading to reflected and diffracted signals, which are known as non-line-of-sight (NLOS) signals. These cause major positioning (also known as "NLOS multipath") errors in GNSS positioning. This paper proposes a novel NLOS multipath detection technique that uses a machine-learning technique to improve positioning accuracy in urban environments. The key idea behind this technique is to construct a classifier that discriminates NLOS multipath signals from the output of the multiple GNSS signal correlators of a software GNSS receiver. In the code-tracking process within GNSS receivers, the code correlation peak is determined and tracked using the outputs of the signal correlators. In the case of an NLOS signal, there are no direct signals; the first reflected signal has low power compared to the direct signal. Hence, the correlation function is expected to be more distorted in the case of NLOS signal correlation. We use this phenomenon to detect NLOS signals. For realizing machine learning, we extract the features of the NLOS signal from the shape of the NLOS correlation function, using an actual dataset, to construct an NLOS classifier. To evaluate the proposed technique, we conduct NLOS classification experiments using signal correlation data acquired at different locations in the Shinjuku area of Japan. We propose to construct an NLOS classifier based on a support vector machine. From the experiments, 87% of the LOS signals and 99% of the NLOS signals are correctly discriminated.

元の言語English
ホスト出版物のタイトル30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
出版者Institute of Navigation
ページ3958-3967
ページ数10
6
ISBN(電子版)9781510853317
出版物ステータスPublished - 2017 1 1
イベント30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017 - Portland, United States
継続期間: 2017 9 252017 9 29

Other

Other30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
United States
Portland
期間17/9/2517/9/29

Fingerprint

Learning systems
Satellites
Navigation
Classifiers
Correlators
Signal systems
Support vector machines
Global positioning system
Experiments

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Electrical and Electronic Engineering

これを引用

Suzuki, T., Nakano, Y., & Aman, Y. (2017). NLOS multipath detection by using machine learning in urban environments. : 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017 (巻 6, pp. 3958-3967). Institute of Navigation.

NLOS multipath detection by using machine learning in urban environments. / Suzuki, Taro; Nakano, Yusuke; Aman, Yoshiharu.

30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017. 巻 6 Institute of Navigation, 2017. p. 3958-3967.

研究成果: Conference contribution

Suzuki, T, Nakano, Y & Aman, Y 2017, NLOS multipath detection by using machine learning in urban environments. : 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017. 巻. 6, Institute of Navigation, pp. 3958-3967, 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017, Portland, United States, 17/9/25.
Suzuki T, Nakano Y, Aman Y. NLOS multipath detection by using machine learning in urban environments. : 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017. 巻 6. Institute of Navigation. 2017. p. 3958-3967
Suzuki, Taro ; Nakano, Yusuke ; Aman, Yoshiharu. / NLOS multipath detection by using machine learning in urban environments. 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017. 巻 6 Institute of Navigation, 2017. pp. 3958-3967
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