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Eigenstructure-based algorithms for direction finding with time-varying arrays

机译:基于特征结构的时变阵列测向算法

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This paper considers the problem of finding the directions of narrowband signals using a time-varying array whose elements move during the observation interval in an arbitrary but known way. We derive two eigenstructure-based algorithms for this problem, which are modifications of techniques developed originally for time-invariant arrays. The first uses array interpolation, and the second uses focusing matrices. Like other eigenstructure-based methods, these algorithms require a modest amount of computations in comparison with the maximum likelihood (ML) estimator. The performance of the algorithms is evaluated by Monte-Carlo simulations, and is compared with the Cramer Rao Bound (CRB). Although both techniques were successful for wideband array processing with time-invariant arrays, we found that only the interpolated array algorithm is useful for direction finding (DF) with time-varying arrays.
机译:本文考虑使用时变阵列查找窄带信号方向的问题,该阵列的元素在观察间隔内以任意但已知的方式移动。我们针对此问题推导了两种基于特征结构的算法,它们是对最初为时不变数组开发的技术的修改。第一个使用数组插值,第二个使用聚焦矩阵。与其他基于特征结构的方法一样,与最大似然(ML)估计器相比,这些算法需要适量的计算。通过蒙特卡洛仿真评估算法的性能,并将其与Cramer Rao Bound(CRB)进行比较。尽管两种技术均能成功地对时不变阵列进行宽带阵列处理,但我们发现只有内插阵列算法可用于时变阵列的测向(DF)。

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