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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Computationally Efficient DOA and Carrier Estimation for Coherent Signal Using Single Snapshot and Its Time-Delay Replications
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Computationally Efficient DOA and Carrier Estimation for Coherent Signal Using Single Snapshot and Its Time-Delay Replications

机译：使用单快照及其延时复制的相干信号的计算上高效的DOA和载波估计

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摘要

In this article, we propose two computationally efficient approaches to jointly estimate direction of arrival (DOA) and carrier for coherent signals using a given single snapshot with uniform linear array. First, the proposed methods construct the Khatri-Rao product-like data vector by introducing $P$ -level delays from the array output. Second, a row elementary transformation is applied to the structured data vector, the rotational invariance structure of the carrier vector is exploited and thereby the carrier is derived. To be able to proceed, the special structure of the data vector is also exploited to estimate the coherent waveform. As a result, the DOA estimation problem can be recast into two Frobenius-norm-based optimization problems: one is referred to as linear search-based covariance-like matrix fitting method, which is suitable for estimating spaced angles that are well separated. The other one is called subspace separation-based covariance-like matrix fitting method, which is suitable for estimating closely spaced angles. The proposed methods develop one-dimensional (1-D) search and $K$ 1-D search procedures, respectively, and hence they are computationally efficient compared to the classical methods. The simulation results illustrate that the performance of the proposed methods is better than the spatial smoothing-based multiple signal classification method under different scenarios.
机译：在本文中，我们提出了两种计算出到达到达方向（DOA）和载波的两种计算有效的方法，用于使用具有均匀线性阵列的给定单一快照的相干信号。首先，所提出的方法通过从阵列输出中介绍$ P $ -Level延迟来构造Khatri-Rao产品样数据矢量。其次，将行基本变换应用于结构化数据向量，利用载波向量的旋转不变性结构，从而导出载波。为了能够继续，还利用数据向量的特殊结构来估计相干波形。结果，DOA估计问题可以重新分为基于Frobenius-Norm的优化问题：一个被称为基于线性搜索的协方差矩阵拟合方法，其适用于估计良好分开的间隔角度。另一个被称为子空间分离的基于协方差矩阵拟合方法，其适用于估计紧密间隔的角度。所提出的方法分别开发一维（1-D）搜索和$ 1-D搜索程序，因此与经典方法相比，它们是计算有效的。仿真结果表明，所提出的方法的性能优于不同场景下的基于空间平滑的多信号分类方法。

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