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2022 | OriginalPaper | Buchkapitel

Jamming Test Scenario Design for Satellite-Based Train Positioning in Railway Signaling Systems

verfasst von : Kang-zhi Xu, Jiang Liu, Bai-gen Cai, Jian Wang, De-biao Lu

Erschienen in: China Satellite Navigation Conference (CSNC 2022) Proceedings

Verlag: Springer Nature Singapore

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Abstract

The implementation of satellite positioning technology in determining the train speed and location has been an important choice to promote the autonomous and intelligent development of new generation railway signaling systems. The vulnerability of satellite positioning makes its positioning performance susceptible to GNSS (Global Satellite Navigation System) jamming signals, which may fail in satisfying the safety requirement of the railway signaling system. Scenario-based GNSS jamming test is an effective approach to evaluate and verify the availability and safety of GNSS-based train positioning. Considering test costs and test coverage requirements of the test scenarios, this paper presents a scenario generation method for jamming test dedicated to the GNSS-based train positioning. The key factors of the test scenarios are analyzed and the AHP (Analytic Hierarchy Process) algorithm is involved to realize the hierarchy division and determine the factor importance. A test case generation algorithm is designed based on the combinatorial testing tool. A continuous test scenario generation framework is established, with which discrete test case clustering is carried out according to the importance of key factors. A specific jamming test platform for GNSS-based train positioning is built, and typical jamming scenarios are adopted for test and verification. The results demonstrate that the proposed solution improves the coverage of the scenarios with a reduced cost level for test operations. It enables the “zero-on-site test” for novel GNSS-based railway signaling systems.

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Metadaten
Titel
Jamming Test Scenario Design for Satellite-Based Train Positioning in Railway Signaling Systems
verfasst von
Kang-zhi Xu
Jiang Liu
Bai-gen Cai
Jian Wang
De-biao Lu
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-2588-7_48

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