Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Although the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low-rank (SLR) and total variation (TV) regularization constraints is proposed in this article. To be specific, a new framework SLR-TV, which mainly contains abnormal signal acquisition, objective function solution, and inverse transform operation is constructed. The noise suppression performance is improved by leveraging the structured low-rankness of the signal. To preserve the clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV with five state-of-the-art methods with extensive synthetic and field tests, the results demonstrate that the proposed SLR-TV method achieves the highest SNR improvement by about 15.62% and the best structural similarity improvement (SSIM) by about 62.95% over other methods in the range from −40 to 0 dB, showing the utility and high-fidelity of the proposed framework in low SNR.