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    郑伟员, 梁子安, 周俊, 等. 水田平地机GNSS高程数据EMD与S-G联合降噪研究[J]. 华南农业大学学报, 2024, 45(1): 80-87. doi: 10.7671/j.issn.1001-411X.202207026
    引用本文: 郑伟员, 梁子安, 周俊, 等. 水田平地机GNSS高程数据EMD与S-G联合降噪研究[J]. 华南农业大学学报, 2024, 45(1): 80-87. doi: 10.7671/j.issn.1001-411X.202207026
    shu
    ZHENG Weiyuan, LIANG Zian, ZHOU Jun, et al. Research on combined noise reduction of GNSS elevation data of paddy field grader with EMD and S-G filter[J]. Journal of South China Agricultural University, 2024, 45(1): 80-87. doi: 10.7671/j.issn.1001-411X.202207026
    Citation: ZHENG Weiyuan, LIANG Zian, ZHOU Jun, et al. Research on combined noise reduction of GNSS elevation data of paddy field grader with EMD and S-G filter[J]. Journal of South China Agricultural University, 2024, 45(1): 80-87. doi: 10.7671/j.issn.1001-411X.202207026
    shu

    水田平地机GNSS高程数据EMD与S-G联合降噪研究

    Research on combined noise reduction of GNSS elevation data of paddy field grader with EMD and S-G filter

      摘要
    • 摘要:
      目的 减小基于全球卫星导航系统(Global navigation satellite system, GNSS)的水田平地机在测量地势高程信息时存在的多路径效应和机器振动影响，提高水田平地前基准面建立的准确度，进而提升平地质量。
      方法 结合经验模态分解(Empirical mode decomposition，EMD)和Savitzky-Golay(S-G)滤波各自的优势，提出了一种EMD与S-G联合降噪的方法。该方法首先采用EMD将原始高程信号分解成若干本征模态函数(Intrinsic mode functions，IMFs)，利用归一化自相关函数和相关系数将其细分为噪声IMFs、混合IMFs和有效IMFs，然后利用S-G算法对混合IMFs进行滤波，最后将S-G滤波后的IMFs与有效IMFs进行重构，得到最终降噪后的数据。
      结果 静态验证试验结果表明，联合滤波后的均方根误差比滤波前降低了36.9%，信噪比比滤波前提高了6.3%。田间测量试验结果表明，滤波后的数据波动范围减少了11.9%。
      结论 EMD与S-G联合降噪算法有效削减了多路径效应误差和振动误差，改善了数据的平滑度，对于提高水田平地作业质量具有现实意义。

       

      Abstract:
      Objective In order to reduce the multipath effect and the influence of machine vibration when measuring terrain elevation information based on the global navigation satellite system (GNSS) paddy field grader, improve the accuracy of the establishment of the datum in front of the paddy field leveling, and then improve the leveling quality.
      Method Combining the respective advantages of empirical mode decomposition (EMD) and Savitzky-Golay (S-G) filter, a combined noise reduction method of EMD and S-G was proposed. The method first used EMD to decompose the raw elevation signal into several intrinsic mode functions (IMFs), and used the normalized autocorrelation function and correlation coefficient to subdivide them into noise IMFs, mixed IMFs and effective IMFs, and the S-G algorithm was used to process the mixed IMFs, and finally the S-G filtered IMFs and the effective IMFs were reconstructed to obtain the final denoised data.
      Result The static verification test results showed that the root mean square error after combined filtering was 36.9% lower than that before filtering, and the signal to noise ratio was 6.3% higher than that before filtering. The field measurement test results showed that the data fluctuation range after filtering was reduced by 11.9%.
      Conclusion The EMD and S-G combined noise reduction algorithm proposed in this paper effectively reduces the multipath effect error and vibration error, improves the smoothness of data, and has practical significance for improving the quality of paddy field leveling operations.

       

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