An Innovative Magnetic Anomaly Detection Algorithm Based on Signal Modulation

Abstract

Magnetic anomaly detection (MAD) is a passive method used to detect the ferromagnetic objects by revealing the anomalies in the ambient Earth magnetic field. This article proposed a novel MAD algorithm based on signal modulation for magnetic sensor array. The new method transforms the original magnetic anomaly into a modulated signal and then detects the target magnetic anomaly signal by using a time–frequency analysis method. Compared with traditional algorithms, the MAD algorithm proposed in this article has three advantages: first, the influence of geomagnetic background is suppressed by calculating the magnetic field difference of the array sensor, and the trend of magnetic field difference is removed by empirical mode decomposition. Second, it properly fuses the magnetic anomaly signal of the sensor array and uses the time–frequency analysis method, which can improve the detection ability of weak magnetic anomaly signals. Third, the new method does not depend on the motion state and characteristics of the target, and it has a good adaptability to the background magnetic field. The simulation results show that this novel detection method has a good ability in the low signal-to-noise ratio (SNR) anomalies detection. For weak magnetic anomaly with an SNR of −6.9 dB, the detection probabilities and false alarm rate are about 0.9648 and 0.001, respectively. The real-world detection experiment proved that this algorithm can detect the weak magnetic anomaly in the geomagnetic background.