An improved BOC signal multi-path suppression method

Wei-huan Wang; Yong-xin Feng; Fang Liu; Xue Li

doi:10.1117/12.2675177

25 May 2023 An improved BOC signal multi-path suppression method

Wei-huan Wang, Yong-xin Feng, Fang Liu, Xue Li

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263636 (2023) https://doi.org/10.1117/12.2675177
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In view of the existing BOC modulated signal synchronization methods have problems such as small application range, limited sidespeak suppression ability and low synchronization probability, and the large code tracking error of BOC modulated signal synchronization in multipath environment, an improved BOC signal multipath suppression method based on code-related reference waveform was proposed. The improved method makes the correlation peak of the cross-correlation function between the BOC modulated signal and the local reference waveform significantly smaller than the correlation peak of the autocorrelation function of the BOC signal by designing the local reference waveform, so as to effectively suppress the error influence caused by the multipath effect. The simulation analysis of BOC signal using narrow correlation method, Double delta method and the new method shows that the new method has better multi-path suppression performance than narrow correlation method and Double delta method, and can almost completely suppress the code loop tracking error caused by multi-path effect in the case of long delay. This study can provide a theoretical reference for the high-precision reception of Beidou navigation signals based on BOC signals, and thus provide a foundation for the enhanced application of satellite navigation system.

Citation Download Citation

Wei-huan Wang, Yong-xin Feng, Fang Liu, and Xue Li "An improved BOC signal multi-path suppression method", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263636 (25 May 2023); https://doi.org/10.1117/12.2675177

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