【摘要】 舰船磁场异常阈值滤波的幅度特征不一,导致异常信号粗提取信噪比值过低。基于此,提出基于大数据技术的舰船磁场异常信号粗提取方法。基于大数据技术设计舰船磁场异常信号粗提取方法,分解舰船异常信号,并从重构的分解异常信号中提取异常特征值。在此基础上,对磁场异常阈值进行滤波识别,对磁场异常阈值滤波的幅度特征进行整合,获取舰船磁场异常信号。至此,完成基于大数据技术的舰船磁场异常信号粗提取方法设计。分析实验结果:采用基于大数据技术的舰船磁场异常信号粗提取方法异常信号粗提取的信噪比值最高为-3 dB,表明所提方法异常信号粗提取效果较好。更多还原

【Abstract】 The amplitude characteristics of threshold filtering of ship magnetic field anomaly are different, which leads to the low signal-to-noise ratio of rough extraction of abnormal signal. Based on this, a rough extraction method of ship magnetic field anomaly signal based on big data technology is proposed. Based on big data technology, a rough extraction method of ship magnetic field abnormal signal is designed, which decomposes the ship abnormal signal and extracts the abnormal eigenvalues from the reconstructed decomposed abnormal signal. On this basis, the magnetic anomaly threshold is filtered and identified, and the amplitude characteristics of magnetic anomaly threshold filtering are integrated to obtain the ship magnetic anomaly signal. So far, the design of rough extraction method of ship magnetic field anomaly signal based on big data technology is completed. Analysis of the experimental results: Based on the big data technology of ship magnetic field abnormal signal rough extraction method, the highest signal-to-noise ratio of abnormal signal rough extraction is-3 dB,which shows that the proposed method is better.更多还原