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针对低信噪比情况下的经典时域波形磁异常检测算法失效问题,提出了基于最小熵滤波器的磁异常检测算法。基于信息熵的计算方法,利用时间轴上的滑动窗构造了最小熵滤波器,然后根据NeymanPearson(NP)定理来确定磁异常检测的阈值,以实现最小熵滤波器磁异常检测算法。仿真实验表明:基于最小熵滤波器的检测算法明显优于经典的时域波形检测算法,并且很大程度上增加了信号的信噪比,提高了磁异常信号的检测可能。
Aiming at the failure problem of classical time-domain waveform anomaly detection algorithm with low SNR, a magnetic anomaly detection algorithm based on minimum entropy filter is proposed. Based on the computing method of information entropy, the minimum entropy filter is constructed by sliding windows on the time axis. Then the threshold of magnetic anomaly detection is determined according to the NeymanPearson (NP) theorem to achieve the minimum entropy filter magnetic anomaly detection algorithm. Simulation results show that the detection algorithm based on the minimum entropy filter is obviously superior to the classical time-domain waveform detection algorithm, and greatly increases the signal to noise ratio of the signal and improves the detection of magnetic anomalies.