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DOA estimation for noninteger linear antenna arrays with more uncorrelated sources than sensors

机译:具有比传感器更多的不相关源的非整数线性天线阵列的DOA估计

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摘要

We investigate direction-of-arrival (DOA) estimation involving nonuniform linear arrays, where the sensor positions may be noninteger values expressed in half-wavelength units, with some number of uncorrelated Gaussian sources that is greater than or equal to the number of sensors. We introduce an approach whereby the (noninteger) co-array is treated as the most appropriate virtual array when considering an augmented covariance matrix. Since such virtual arrays have an incomplete set of covariance lags, we discuss various completion philosophies to fill in the missing elements of the associated partially specified Hermitian covariance matrix. This process is followed by the application of an algorithm that searches for a specific number of plane wavefronts, yielding the minimum fitting error for the specified covariance lags in the neighborhood of the completion-initialized DOA estimates. In this way, we are able to resolve possible ambiguity and to achieve asymptotically optimal estimation accuracy. Numerical simulations of DOA estimation demonstrate a close proximity to the Cramer-Rao bound.
机译:我们研究了涉及非均匀线性阵列的到达方向(DOA)估计,其中传感器位置可能是以半波长单位表示的非整数值,并且一些不相关的高斯光源大于或等于传感器的数量。我们介绍一种在考虑增强协方差矩阵时将(非整数)协数组视为最合适的虚拟数组的方法。由于此类虚拟数组具有一组不完整的协方差滞后,因此我们讨论了各种完成原理,以填充关联的部分指定的Hermitian协方差矩阵的缺失元素。在此过程之后,应用一种算法,该算法搜索特定数量的平面波前,从而在完成初始化的DOA估计值附近产生指定协方差滞后的最小拟合误差。通过这种方式,我们能够解决可能的歧义并实现渐近最优估计精度。 DOA估计的数值模拟表明与Cramer-Rao界非常接近。

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