Multi-Modal Time-Frequency Image Fusion for Weak Target Detection on Sea Surface

Aiming at the problem of harrowing target feature extraction for one-dimensional radar signals in the strong sea clutter background, this paper proposes a weak target detection method based on the combination of multi-modal time-frequency map fusion and deep learning in the sea clutter background. The one-dimensional signal is converted into three gray-scale maps with complementary characteristics by three signal processing methods: normalized continuous wavelet transform, Normalized Smooth Pseudo Wigner-Ville Distribution, and recurrence plot; the resulting two-dimensional grayscale maps are adaptively mapped to the R, G, and B channels through an adaptive weighting matrix for feature fusion, ultimately generating a fused color image. Subsequently, an improved multi-modal EfficientNetV2s classification framework was constructed, wherein the decision threshold of the Softmax layer was optimized to achieve controllable false alarm rates for weak signal detection. Experiments are carried out on the IPIX dataset and the China Yantai dataset, and the proposed method achieves certain improvement in detection performance compared with existing detection methods.