Aerial Magnetic Anomaly Target Detection Method Based on Multi-Scale Orthonormal Basis Function Convolution and Convolution-Augmented Gated Attention

Haozhe Chen; Ke Zhang; Wanhua Zhu;

HIGHLIGHT

To address the issues of traditional Orthonormal Basis Function (OBF) detection relying on prior knowledge of the closest contact distance and susceptibility to mismatch with single-scale templates, this paper proposes an aerial magnetic anomaly target detection method that integrates multi-scale OBF with a convolution-augmented gated attention unit (CGAU).

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ABSTRACT

Magnetic anomaly detection from aerial platforms enables passive, non-contact detection of underwater ferromagnetic targets. To address the issues of traditional Orthonormal Basis Function (OBF) detection relying on prior knowledge of the closest contact distance and susceptibility to mismatch with single-scale templates, this paper proposes an aerial magnetic anomaly target detection method that integrates multi-scale OBF with a convolution-augmented gated attention unit (CGAU). Six sets of fixed OBF convolution kernels each output three matching responses, which are concatenated with the original sequence to form 19-channel features. Four layers of one-dimensional (1-D) convolution and two stages of CGAU extract local waveform features and capture long-range dependencies. The precision (PC), detection rate (DR), and F1-score of the proposed network on the simulated test set are 94.70%, 95.11%, and 94.90%, respectively (best-performing seed = 42). On the measured dataset, it detected 11 of 12 overpass events, achieving an event detection rate of 91.67%. The proposed network has approximately 0.43 M trainable parameters and achieves a higher mean F1 across three random seeds than the seven selected baselines, demonstrating its potential for aerial magnetic anomaly target detection.

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