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常规多信号分类(MUSIC)在估计信号或噪声子空间时未利用阵列的方向矢量信息。为改善波达方向(DOA)估计性能,提出一种新的角信号子空间概念。首先,由Gram行列式和超维空间中多面体体积公式,给出常规MUSIC方法的几何解释。其次,利用阵列响应矢量扩展观测数据矩阵,在每个搜索方向由增广数据矩阵的奇异值分解获得角信号子空间估计。理论分析表明,常规MUSIC零谱相当于超维空间中由阵列观测数据矢量和搜索方向矢量决定的多面体体积。仿真实验表明,利用角信号子空间能够较明显地改善DOA估计性能,特别是信号相关、信噪比较低以及快摄数较小的情况。
The conventional multi-signal classification (MUSIC) does not utilize the array’s directional vector information in estimating the signal or noise subspace. In order to improve DOA estimation performance, a new concept of angular signal subspace is proposed. First, the geometrical interpretation of the conventional MUSIC method is given by the formula of the volume of the polyhedron in the Gram determinant and the super-dimensional space. Secondly, using the array response vector to expand the observed data matrix, the angular signal subspace estimation is obtained from the singular value decomposition of the augmented data matrix in each search direction. Theoretical analysis shows that the conventional MUSIC zero spectrum is equivalent to the volume of the polyhedron in the super-dimensional space determined by the array observation data vector and the search direction vector. Simulation results show that the use of angular signal subspace can significantly improve the performance of DOA estimation, especially the signal correlation, the low signal-to-noise ratio and the small number of snapshots.