极化分布源模型及角度估计

通过对“分布源是在一定空间分布的多个点源信号的叠加”的分析，提出了极化分布源模型和一阶近似的极化分布源模型。对极化分布源模型，为减少噪声子空间拟合算法(NSF)的搜索空问雏数，文中提出只搜索角度空间求解到达角，对极化参数通过求解线性方程组获得；对一阶近似的极化分布源模型，利用旋转予空间不变(ESPRIT)类算法求解到达角，极化参数同样通过求解线性方程组获得。仿真结果表明：极化的引入对NSF的性能有很大提高，以及一阶近似模型对角度分布的盲性。

Polarized Distribution Source Model and Their DOA Estimation

A polarized distribution source model is presented by the fact i.e. a spatial distribution source is superposed by many point sources in a continuous spatial area. And the one-order approximate model for polarized distribution sources is also introduced. To decrease space-searching dimensions of noise subspace fitting (NSF) for a polarized distribution source model, the angular dimensions are searched by the algorithm to estimate direction of arrivals (DOAs), and then results of the polar parameters by linear equations are obtained. For the one-order approximate model, this paper uses a class of ESPRIT to estimate DOAs and the polar parameters by linear equations. Simulational results demonstrate that the models and respective algorithms are effective. Finally, simulational results indicate that the performance of NSF is improved by polarization and the one-order approximate model is blinded to angular distributions.