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传统的空间谱估计测向方法大都是在二阶统计基础上提出的。而高阶累积量(cumulant)及其对应的高阶谱具有更加丰富的信息,而且对任何谱特性的高斯噪声都有很好的抑制能力。本文研究了基于四阶累积量特征结构分析的谱估计测向方法。从累积量的基本定义和性质出发,导出了基于特征分解的测向算法,并给出了模拟实验和测向系统外场实验结果。实验结果显示,基于四阶累积量方法的性能优于传统谱估计测向方法,特别是对于未知谱特性的色高斯噪声的情况。
The traditional methods of spatial spectrum estimation and direction finding are mostly based on the second-order statistics. The higher order cumulant and its corresponding higher order spectrum have more information, and have good ability to suppress any spectral characteristic of Gaussian noise. In this paper, we study the spectral estimation method based on the fourth-order cumulant feature structure analysis. Based on the basic definition and properties of cumulants, a DF algorithm based on eigendecomposition was derived, and the experimental results of simulations and field experiments were given. The experimental results show that the performance based on the fourth-order cumulant method is superior to the traditional spectral estimation and direction finding method, especially for the Gaussian noise with unknown spectral characteristics.