Magnetic Anomaly Detection Method with Empirical Mode Decomposition and Minimum Entropy Feature

Magnetic anomaly detection (MAD) is a passive method for airborne detection of subsurface objects. Light, radar and sound waves are unable to pass through the open air into the sea water environment and penetrate deep into the water, weaken or return to the air environment. On the other hand, the magnetic field force lines on this boundary are unchanged. MAD method is based on measuring the smallest changes or anomalies caused by the earth's magnetic field due to the passage of a ferro-magnetic object and the magnetic bipolar field generated around it, and especially in shallow waters, is one of the most efficient methods. Due to the rapid reduction of the magnetic field by increasing the distance, the magnetic disturbance generated by the magnetic target in the distance, usually buried in magnetic noise, in other words, the signal-to-noise ratio (SNR) decreases. In this paper, in order to improve the detection performance of magnetic impairment in low SNR, a hybrid method of MAD based on empirical mode decomposition (EMD) and minimal entropy method is proposed. In order to evaluate the performance of the method, an electronic measuring device has been constructed and magnetic data have been fieldly harvested from caspian sea in Anzali port area. These data impregnated with environmental magnetic noise have been investigated by entropy method. According to the entropy feature, magnetic anomaly is detected whenever entropy degrades below the defined threshold. In this way, the proposed method for detecting weak magnetic anomalies is also effective. The test results indicate a high probability of detection of the proposed method for low input SNR. Compared to the original signal SNR with -10 dB, the reconstructed signal SNR has improved to 8 dB. In addition, the total time of updating the parameters of the probability density function (PDF ), of noise is about 0.075s obtained.