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Asymptotic performance analysis of direction-finding algorithms based on fourth-order cumulants

机译:基于四阶累积量的测向算法的渐近性能分析

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In the narrow-band array processing context, the use of higher order statistics has been often advocated because consistent and asymptotically unbiased parameter estimates can be obtained without it being necessary to know, to model, or to estimate the spatial covariance of the noise as long as it is normally distributed. However, experimentation shows that this 'noise insensitivity' is traded for increased variability of the parameter estimates. The main purpose of this contribution is to derive and work out closed-form expressions of the asymptotic covariance of MUSIC-like direction-of-arrival estimates based on two fourth-order cumulant matrices: the diagonal slice and the contracted quadricovariance. This is compared with the standard covariance-based MUSIC estimate establishing on a rational basis the domain of applicability of higher order statistics for DOA estimation. In particular, the actual impact of the noise variance and of the dynamic range of the sources is investigated. This asymptotic performance analysis is achieved within a general framework, which we believe to be of general interest.
机译:在窄带阵列处理环境中,经常提倡使用高阶统计量,因为可以获得一致且渐近的无偏参数估计,而不必知道,建模或估计噪声的空间协方差。因为它是正态分布的。但是,实验表明,这种“噪声不敏感”被换成了参数估计值增加的可变性。此贡献的主要目的是基于两个四阶累积量矩阵:对角切片和收缩二次方差,得出并计算出MUSIC类到达方向估计的渐近协方差的闭式表达式。将其与基于标准协方差的MUSIC估计进行比较,该估计在合理的基础上确定了用于DOA估计的高阶统计的适用范围。特别是,研究了噪声方差和源动态范围的实际影响。这种渐进性能分析是在一个总体框架内实现的,我们认为这是普遍感兴趣的。

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