The prediction and suppression of sea clutter have long been challenging issues in the field of maritime target detection. Traditional sea clutter prediction algorithms often struggle to effectively model complex sea clutter data. When predicting sea clutter based on temporal correlations, the clutter data from adjacent range cells can influence each other, resulting in low prediction accuracy. To address these challenges, this paper analyzes the spatio-temporal correlation of sea clutter and designs a spatio-temporal prediction network based on a self-attention mechanism, which comprehensively analyzes sea clutter data across multiple range cells. Subsequently, the cross-spectrum was employed to compare the frequency coherence between the predicted sea clutter and the measured sea clutter. A bandpass filter was then designed to accurately suppress the clutter by targeting the specific frequency band of the target signal. The proposed spatio-temporal prediction model and suppression algorithm were evaluated using three sea clutter measured datasets collected from IPIX radar, X-band radar, and UHF-band radar. The results demonstrate that, compared with a temporal LSTM model, the proposed prediction algorithm reduced the overall average mean squared error (MSE) by 0.06 and improved prediction accuracy by approximately 26.18%, while the suppression algorithm effectively enhanced the signal-to-clutter ratio (SCR) of the radar echo, achieving an improvement of 17.03 dB.

Cross-medium acoustic–radio collaborative networks enable integrated communication among underwater, surface, and aerial nodes for marine observation and detection. However, heterogeneous propagation characteristics of acoustic and radio channels significantly degrade the performance of conventional single-medium routing protocols, resulting in excessive control overhead, a low packet delivery ratio (PDR), and high latency. To address these challenges, this paper proposes an optimized AODV protocol for Cross-medium Acoustic–Radio Collaborative Networks (CACN-OAODV). The proposed protocol incorporates a medium-aware routing initiation mechanism to reduce unnecessary broadcasts, a link stability factor that jointly considers hop count and channel quality for reliable path selection, and a lightweight control optimization scheme to limit routing overhead in acoustic environments. Extensive simulations conducted in NS-3 with realistic multi-channel propagation models demonstrate that CACN-OAODV significantly outperforms the standard AODV protocol, achieving improved PDR, higher throughput, and reduced end-to-end delay. These results indicate that CACN-OAODV provides an effective routing solution for heterogeneous cross-medium marine communication networks.

Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification. To address the challenge of existing filter parameters being unable to adapt in real-time to the characteristics of sea clutter, this paper integrates ocean numerical models into the sea clutter spectrum estimation. By adjusting filter parameters based on the spectral characteristics of sea clutter, the accurate suppression of sea clutter is achieved. In this paper, the Weather Research and Forecasting (WRF) model is employed to simulate the ocean dynamic parameters within the radar detection area. Hydrological data are utilized to calibrate the parameterization scheme of the WRF model. Based on the simulated ocean dynamic parameters, empirical formulas are used to calculate the sea clutter spectrum. The filter coefficients are updated in real-time using the sea clutter spectral parameters, enabling precise suppression of sea clutter. The suppression algorithm is validated using X-band radar-measured sea clutter data, demonstrating an improvement factor of 17.22 after sea clutter suppression.