An Improved Recognition Technique for Ship Targets Based on Dual-Path Cooperative Fusion Mechanism

With the increasing complexity of the electromagnetic environment, traditional radar target recognition methods face severe challenges. High-resolution range profile (HRRP) and Radar Cross Section (RCS), as two important radar features, each has its own advantages in target recognition but also exhibits limitations. To enhance radar target recognition performance in complex scenarios such as low signal-to-noise ratio (SNR), this paper proposes a recognition method based on heterogeneous multi-modal feature fusion. The proposed method constructs a three-channel parallel encoding network, which utilizes Convolutional Long Short-Term Memory (ConvLSTM), One-Dimensional Convolutional Gated Recurrent Unit (Conv1D-GRU), and Gated Recurrent Unit (GRU) to extract deep discriminative features from raw HRRP sequences, RCS sequences, and HRRP statistical features, respectively. Furthermore, it innovatively designs a dual-path cooperative fusion mechanism, achieving explicit inter-modal correlation modeling through a cross-attention module and dynamically learning the importance of each modality through an adaptive weight fusion layer, thereby realizing deep complementarity and enhancement of multi-modal information. Experimental results demonstrate that under various signal-to-noise ratios and polarization conditions, the proposed method achieves a maximum average recognition accuracy of over 99% for 6 ship targets. Compared with the traditional three-channel fixed-weight fusion method, the recognition accuracy of the proposed method increases from 90.01% to 99.42% under co-polarization, and from 81.14% to 98.15% under cross-polarization, fully validating the effectiveness and superiority of the dual-path fusion mechanism.