Research on ELoran Demodulation Algorithm Based on Multiclass Support Vector Machine

Author:

Liu Shiyao12,Yan Baorong12,Guo Wei12,Hua Yu12,Zhang Shougang134,Lu Jun5,Xu Lu5ORCID,Yang Dong6

Affiliation:

1. National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China

2. Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi’an 710600, China

3. University of Chinese Academy of Sciences, Beijing 100039, China

4. Key Laboratory of Time and Frequency Standards, Chinese Academy of Sciences, Xi’an 710600, China

5. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China

6. Sichuan Meteorological Service Centre, Chengdu 610072, China

Abstract

Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.

Funder

Key Research and Development Project of Sichuan Science and Technology Department

Soft Science Project of China Meteorological Administration

Natural Science Foundation of Sichuan Province

Publisher

MDPI AG

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