An Enhanced Magnetic Anomaly Detection Method Based on Probability Density Similarity

Magnetic anomaly detection (MAD) technology, which identifies concealed ferromagnetic targets by analyzing weak perturbations in the geomagnetic field, holds significant value in unexploded ordnance (UXO) identification, underwater target detection and related fields. To address the performance limitations of existing non-prior detection methods in low signal-to-noise ratio (SNR) scenarios, this study establishes a novel enhanced MAD framework through comparative analysis of probability density functions (PDFs) between reference signals and target signals, coupled with multi-metric similarity assessment, enabling reliable MAD in both single-sensor and dual-sensor configurations. Theoretical analysis and field experiments show that at SNR = -4 dB, the proposed method can improve the detection probability by 12.7% and 26.2%, respectively, relative to the parallel stochastic resonance (PSR) method and the minimum entropy detection (MED) method. This approach transcends the information dimensionality constraints of traditional entropy features, establishing a new paradigm for prior-free MAD in complex magnetic environments through multi-scale probability density feature extraction.