With the increasing demand for surveilling lowobservable targets, dim target detection in complex ocean environments remains a challenging issue. Sea clutter suppression can significantly enhance target detection performance. However, the complexity and variability of the ocean environment causes the sea clutter deviate from prior assumptions, leading to suboptimal performance of empirical model-based suppression methods. Meanwhile, data-driven sea clutter suppression methods often fail to generalize across varying sea state levels. In this work, we propose an adaptive sea clutter suppression method based on meta learning. The proposed approach takes the Range-Doppler spectrum of sea clutter as a multi-channel input to the autoencoder and employs unsupervised reconstruction as a proxy task to metatrain the base model. Under different environmental conditions, the model can update using few shot samples from the unseen scenario to adapt to the current sea clutter suppression task, demonstrating strong generalization ability. The proposed method surpasses representative sea clutter suppression methods in terms of signal-to-clutter ratio improvement and detection probability. It also exhibits superior performance in the Intersection-over-Union metric. Generalization ability test results indicate that the proposed method effectively suppresses sea clutter across various sea state conditions.

Long-time coherent integration is an effective method for dim target detection from heavy sea clutter. To detect dim targets, a novel long-time coherent integration method based on segmented compensation is proposed in this paper. The method models the complex motion of a marine target as the combination of multi-stage uniformly accelerated motions. According to the difference of energy distribution in Doppler frequency domain, this method can suppress sea clutter and detect the regions of interest (ROIs). Using time–frequency domain energy analysis, the potential target can be extracted. After estimating the parameters and segmentation, for the potential targets, the phase compensation factor can be used to eliminate the Doppler frequency modulation caused by the complex motion. Finally, for the compensated signal, long-time coherent integration is performed to realize the target detection and discrimination under low signal-to-clutter ratio. To verify the effectiveness of the proposed method, we apply simulation data and measured CSIR data in the experiments. The results show that the proposed method can integrate the energy of target more effectively than MTD and RFrFT, and the novel method has better detection performance for complex moving targets under low signal-to-clutter ratio situation.