The transponder-style deception jamming implemented by Digital Radio Frequency Memory (DRFM) exhibits high similarity to real target radar echoes, while traditional detection methods suffer severe performance degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a DRFM active deception jamming detection method based on diagonal integral bispectrum, aiming to overcome the bottleneck of jamming detection under low-SNR conditions. By establishing a harmonic effect signal model for DRFM deception jamming, the cross-term generation mechanism in the bispectrum domain is revealed: the jamming signal generates dense cross-terms due to harmonic distortion, whereas the real target energy exhibits single-peak aggregation. To quantify this difference, the Diagonal Integral Bispectrum Relative Peak Height (DIBRP) is proposed to characterize the energy aggregation of true and false targets in the diagonal integral bispectrum, and the Diagonal Integral Bispectrum Approximate Entropy (DIBAE) is introduced to describe their complexity. A joint detection framework combining the DIBRP-DIBAE dual-feature space and a polynomial kernel support vector machine (SVM) is constructed to achieve active deception jamming detection. The proposed method demonstrates excellent performance under low-SNR conditions. Simulations and experimental results show that the correct detection rate reaches 92% at a jamming-to-signal ratio (JSR) and SNR of 0 dB, validating the effectiveness of the algorithm.

Sea surface non-stationarity poses significant challenges to sea-surface small target detection, particularly in maintaining a stable false alarm rate (FAR). In dynamic maritime scenarios with non-stationary characteristics, the non-stationarity of sea clutter can easily cause significant changes in the clutter feature space, leading to a notable deviation between the preset FAR and the measured FAR. By analyzing the temporal and spatial variations in sea clutter, we model the relationship between the preset FAR and the measured FAR as a two-parameter linear function. To address the impact of sea surface non-stationarity on FAR, the model parameters are estimated in real time within the environment and used to guide the dynamic adjustment of the decision region. We applied the proposed method to both convex hull and support vector machine (SVM) detectors and conducted experiments using measured X-band sea-detecting datasets. Experiments demonstrate that the proposed method effectively reduces the deviation between the measured mean FAR and the preset FAR. When the preset FAR is 10⁻², the proposed method achieves an average FAR of 1.067 × 10⁻² with the convex hull detector and 1.043 × 10⁻² with the SVM detector.

The joint detection of multiple features significantly enhances radar’s ability to detect weak targets on the sea surface. However, issues such as large data requirements and the lack of robustness in high-dimensional decision spaces severely constrain the detection performance and applicability of such methods. In response to this, this paper proposes a radar target detection method based on linear fusion of two features from the perspective of feature dimension reduction. Firstly, a two-feature linear dimensionality reduction method based on distribution compactness is designed to form a fused feature. Then, the generalized extreme value (GEV) distribution is used to model the tail of the probability density function (PDF) of the fused feature, thereby designing an asymptotic constant false alarm rate (CFAR) detector. Finally, the detection performance of this detector is comparatively analyzed using measured data.

In addressing the issue of weak target detection at sea, most existing feature detection methods are designed for scenarios with low sea states and small grazing angles. Under high sea states and large grazing angles, variations in scattering mechanisms lead to changes in feature characteristics, resulting in performance degradation when these methods are applied directly due to scene mismatch. To address this, this paper employs four quantitative metrics—mean feature value, coefficient of variation, Bhattacharyya distance, and Spearman correlation coefficient—to analyze the centrality, variability, separability, and correlations of nine features in the time, frequency, and time-frequency domains under varying sea states and grazing angles. The study reveals that, with increasing sea state and changing grazing angles, the separability of time-frequency features, especially the time-frequency ridge accumulation, declines more gradually than other features, and feature correlations generally weaken. These findings provide a reference for joint feature detection in complex scenarios. To optimize feature application, the Spearman correlation coefficient matrix was transformed into a generalized distance matrix, and spectral clustering was used to group features with strong correlations. Feature selection was then performed from the clusters based on mean feature value, coefficient of variation, and Bhattacharyya distance, yielding an optimal feature set for the current scenario. Validation on the SDRDSP dataset under sea states 4–5 showed that the proposed method achieved an average detection probability 10.64% higher than existing methods. Further validation on the Yantai angle airborne test dataset, with grazing angles ranging from 62° to 82°, showed an average detection probability increase of 10.07% over existing methods.