Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Se- condly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of the advantages of M/K detector and integrating detector, a robust detector based on scatterer density （DBSD） is designed, which can reduce the probable collapsing loss or quantization error ef- fectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indi- cate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD out- performs the existing scatterer-density-dependent detector.
从合成孔径雷达(SAR)成像模型出发,在稀疏条件下,该文结合散射中心理论,从低分辨率图像中估计高分辨率图像的散射点参数,用若干sinc函数对感兴趣目标区(ROI)进行重建并抑制旁瓣,获得超分辨ROI切片。基于非线性最小二乘(NLS)估计给出了该超分辨重建问题的迭代求解算法,并以Terra SAR-X数据进行仿真验证,仿真结果表明,该文所提方法相比双立方插值和1范数正则化方法能够获得更高的空间分辨率与目标杂波比(TCR)。后续分析表明,散射点参数的估计精度受到信噪比和sinc函数重建3 d B带宽共同影响,重建3 d B带宽越大对噪声的鲁棒性越强。
