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A novel subspace partition method for fourth-order statistics based MUSIC algorithm

机译:基于MUSIC算法的四阶统计子空间划分新方法

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摘要

It is known that accurate partition of subspaces is important to fourth-order statistics based multiple signal classification (FO-MUSIC) algorithm. However, when the number of signals exceeds the number of array elements, the error of conventional subspace partition method will increase, and the performance of FO-MUSIC algorithm will decrease. A novel subspace partition method for FO-MUSIC algorithm is proposed in this paper. Subspaces can be divided properly by the proposed method according to the way of array expansion. Good performance of FO-MUSIC algorithm can be obtained. The validity of the proposed method is verified by simulation results.
机译:众所周知,子空间的精确划分对于基于四阶统计的多信号分类(FO-MUSIC)算法很重要。然而,当信号数量超过阵列元素数量时,传统子空间划分方法的误差将增加,而FO-MUSIC算法的性能将下降。提出了一种新的FO-MUSIC算法子空间划分方法。子空间可以根据阵列扩展的方式用所提出的方法适当地划分。可以获得良好的FO-MUSIC算法性能。仿真结果验证了该方法的有效性。

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