The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter.

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.

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.

Reliable weak target detection in nonstationary sea clutter is essential for maritime surveillance and navigation safety. Fluctuating sea-surface returns and evolving Doppler spectra can mask weak targets, while conditioning solely on the cell under test may introduce target components into the background representation. This paper proposes a reference-cell Doppler-conditioned Mamba autoencoder (RDC-Mamba) for unsupervised weak target detection. Time–frequency features from reference cells outside the guard regions are aggregated through masked attention and adaptive bilateral fusion to form a local Doppler prior. This prior modulates the state-update step size, input mapping, and output gate, linking temporal modeling to the surrounding clutter background. The network is trained on target-free clutter samples, and detection combines temporal complex-valued and multi-resolution spectral reconstruction errors. At a false-alarm probability of ${10}^{-3}$, RDC-Mamba outperforms classical CFAR detectors and the evaluated reconstruction-based baselines, achieving detection probabilities of 84.6% and 88.2% on IPIX and SDRDSP, respectively. Compared with Self-Doppler Mamba, the strongest competing baseline, the detection probabilities increase by 7.0 and 5.2 percentage points, respectively.