基于高斯方向图的二维子阵级相干源超分辨测向

2-D super-resolution direction finding at subarray level for coherent sources based on Gaussian patterns

二维子阵级超分辨测向在相控阵雷达中具有重要应用.本文研究适用于相干源的子阵级ML估计方法.提出了子阵级ML估计的信号模型.利用子阵相位中心与增益来构造简化的阵列流形,使相控阵的校正成本与代价得到较大的降低.引入加权网络对子阵输出进行后处理,大大提高了阵列处理的灵活性.构造了高斯模式的子阵方向图.与直接简化的阵列流形方法相比,基于高斯方向图的简化阵列流形方法克服了其测向范围无法调整的局限性,且能更好抑制旁瓣源.仿真结果证实了所提出方法的有效性.

2-D super-resolution direction finding at subarray level has important applications in phased array radars. The ML estimation method at subarray level suited for coherent sources is studied and the corresponding signal model is presented. Constructing simplified array manifolds by using the subarray phase centers and gain can reduce the calibration cost and expense of phased array greatly. We introduce weighting network to post-process the subarray outputs and that makes the array processing more flexible. The Gaussian subarray patterns are constructed and the method based on simplified array manifold with them is presented. Compared with the method based on direct simplified array manifold ,this method can adjust the available region of direction estimation and suppress the sidelobe sources better. Simulation results express the validity of the proposed methods.