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.Secondly,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 effectively.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 indicate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities.It is also shown that the DBSD outperforms 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。
