多天线多载波系统的低复杂度 DoA-ToA 信号估计

Chandrashekhar Rai, Debarati Sen
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引用次数: 0

摘要

精确的到达方向(DoA)和到达时间(ToA)估计是声纳、雷达、通信和双功能雷达通信(DFRC)等多种无线系统的严格要求。由于需要使用高载波频率和带宽,这些系统大多采用多天线和子载波设计。虽然大阵列系统的分辨率很高,但由于频谱泄漏效应,实用的网格上估计方法的 DoA-ToA 估计精度仍然存在估计不准的问题。本文提出了具有正交频分复用(OFDM)信号的多天线多载波系统的 DoA-ToA 估计方法。在第一种方法中,我们应用了基于离散傅里叶变换(DFT)的粗特征估计,并提出了一种低复杂度多级微调方法,以极大地提高估计精度。第二种方法基于压缩传感,我们通过获取比实际天线和子载波基数更完整的二维角度-延迟字典来实现超分辨率。与矢量化 1D-OMP 方法不同的是,我们在矩阵数据模型上应用了低复杂度 2D-OMP 方法,这使得 CS 方法在大型阵列环境中的应用变得切实可行。通过数值模拟,我们发现我们提出的方法与基于子空间的 2D-MUSIC 方法性能相似,但计算复杂度显著降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems
Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity.
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