自适应加权修正的强弱信号Capon谱估计方法

Capon空间谱需要计算搜索方向矢量与训练样本协方差矩阵的广义内积值，要求有较高的协方差矩阵估计精度。在训练样本较少且同时存在强弱临近目标条件下，采样协方差矩阵存在较大估计误差，使得空间谱分辨性能严重下降。提出自适应加权修正的改进方法，所提方法在重构数据协方差矩阵基础上，首先利用最小二乘方法估计搜索信号强度并修正数据协方差矩阵，然后计算搜索方向矢量在相应修正的数据协方差矩阵的广义内积值，最后利用该广义内积值对传统Capon空间谱进行自适应加权处理，在保持高分辨性能的同时降低了对样本数的要求，提高了临近强弱目标的稳健性。理论分析和仿真结果表明所提方法在小训练样本条件下对临近强弱信号的分辨性能优于Capon方法。

Modified Capon approach with adaptive weighted for discriminating strong and weak signals

The special spectral estimation using Capon approach can be achieved by measuring the generalized inner product (GIP) value between each search steering vector and the correlation matrix (CM). Therefore, the CM deviation, which is induced by the secondary data number is comparable with the sensor number and the power of one signal is much stronger than that of the others, will deteriorate the performance of Capon algorithm. To alleviate the performance degradation, a new modified Capon method is presented. Firstly, the CM is estimated with iterative approach which effectively relieves the requirement of a large number of training samples. Secondly, a modified correlation matrix at each search steering vector to calculate the GIP value is employed. Finally, the spatial spectrum can be obtained as weighted Capon spectrum with the GIP value. The high resolution property of Capon approach is remained, meanwhile the robustness against small sample support and strong and weak signal coexistence is also enhanced. Theoretical analysis and numerical simulation indicate that its performance is better than that of Capon.