A Radar Target Feature Detection Method Based on Random Permutation of Fractional Fourier Transform Spectrum Matrix

ABSTRACT

Traditional coherent integration detection methods based on the fractional Fourier transform (FRFT) suffer from high redundancy and difficulty in controlling the false alarm probability during detection fusion. Meanwhile, existing feature detection methods that utilise the maximum singular value of the FRFT spectrum matrix fail to consider the impact of spectral mean and spectral structural information on the maximum singular value and its robustness. To address these issues, this paper introduces the idea of random permutation of matrix elements and proposes a radar target feature detection method based on random permutation of the FRFT spectrum. On the one hand, the proposed method suppresses the influence of the background mean on the maximum singular value through decentring processing. On the other hand, it improves the robustness of the maximum singular value feature via random permutation. Finally, simulation and measured data are used to conduct a comparative analysis between the proposed method and three existing methods, namely, the noncoherent integration detection method, the FRFT‐domain coherent integration detection method and the feature detection method based on the maximum singular value of the FRFT spectrum matrix. The results demonstrate that the proposed method outperforms the three existing methods in terms of detection performance under the backgrounds of complex Gaussian noise and low sea‐state sea‐clutter.