优化ICEEMDAN的工频磁异常信号去噪方法

Optimized ICEEMDAN Power Frequency Magnetic Anomaly Signal Denoising Method

针对复杂海洋环境下干扰噪声极大影响工频磁异常信号识别的问题,该文提出一种优化改进的自适应噪声完备集合经验模态分解(improved complete ensemble EMD with adaptive noise,ICEEMDAN)的工频磁异常信号去噪算法。利用麻雀搜索算法(sparrow search algorithm,SSA)对ICEEMDAN的两个关键参数进行寻优,不仅通过迭代分解消除了模态混淆现象,而且克服了人为经验选取参数造成的时间损失和效果偏差的问题。将其应用于仿真和实际工程的算例中,结果表明,与传统ICEEMDAN方法进行对比,所提方法不仅将信噪比提升了22.6 dB,而且显著还原了信号的本征特点,表明所提方法有利于获得更加清晰且准确的数据,为后续高效准确地探测水下目标奠定了良好基础。

Aiming at the problem that the interference noise greatly affects the detection of power frequency magnetic anomaly signal in complex marine environment,an improved adaptive noise complete ensemble empirical mode decomposition denoising algorithm for power frequency magnetic anomaly signal is proposed.The sparrow search algorithm(SSA)is used to optimize the two key parameters of ICEEMDAN,which not only eliminates the modal confusion phenomenon through iterative decomposition,but also overcomes the problem of time loss and effect deviation caused by human experience in selecting parameters.It is applied to simulation and practical engineering examples.The results show that compared with the traditional ICEEMDAN method,the proposed method not only improves the signal-to-noise ratio by 22.6 dB,but also significantly restores the intrinsic characteristics of the signal.It shows that the proposed method is conducive to obtaining clearer and more accurate data,which lays a good foundation for the subsequent efficient and accurate detection of underwater targets.