A target detection method based on polarimetric multi-domain feature fusion is proposed in this paper to improve the detection performance of slow small targets on the sea. Firstly, a complex symmetric matrix was established based on the Pauli scattering vector. On the basis of an analysis on the matrix, the Takagi decomposition method was adopted to extract the normalized polarimetric maximum eigenvalue to characterize the echo signal. Secondly, a real symmetric Hurst exponent matrix was constructed by processing the echo signal of the polarimetric radar, and the normalized polarimetric Hurst exponent was extracted by the eigenvalue decomposition method. Thirdly, the normalized polarimetric Doppler peak height was extracted through the Doppler peak height algorithm. Finally, by fusing multi-domain features, a false alarm controllable detector was constructed through the convex hull algorithm. The results of experimental analysis on the measured datasets indicate that when the parameters are the same, compared with the traditional detection methods based on polarimetric features, the proposed method presents better robustness in the case of short observation time and low signal to clutter rate.
"Sea-Surface Slow Small Target Detection Based on Polarimetric Multi-Domain Feature Fusion," Progress In Electromagnetics Research M,
Vol. 110, 185-195, 2022. doi:10.2528/PIERM22031701
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