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.