Hardware-Efficient Direction of Arrival Estimation using Compressive Sensing
Hardware-Efficient Direction of Arrival Estimation using Compressive Sensing
复制标题
使用压缩感知的硬件高效到达方向估计
DOI：
--
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Berkan Kiliç
中科院分区：
文献类型：
--
作者：
A. Güngör;Berkan Kiliç
基于压缩感知（CS）的波达方向（DOA）估计能够在降低硬件复杂度的情况下实现高性能。影响这种系统性能的一个重要方面是用于减少采样器数量的测量矩阵。最近的研究提出了设计CS-DOA特定的测量矩阵。然而，这些研究假设每个传感器都连接到每个采样器通道。这种设计减少了模数采样器的数量，同时增加了混合传感器输出所需的其他模拟组件的数量。在这项研究中，我们解决的问题，硬件有效的测量矩阵设计CS基于DOA估计。首先，我们提出构建一个硬件有效的测量矩阵投影到块对角矩阵的硬件效率低下的全矩阵。我们严格推导出必要的方程，并提供建议的投影操作的解析解。接下来，我们提出了一个结构化的随机置换的传感器，以最大限度地提高全矩阵和块对角矩阵之间的相似性。我们彻底验证我们提出的方法，通过比较它以前提出的块对角高斯随机矩阵下的各种模拟设置。
Compressed sensing (CS) based direction of arrival (DOA) estimation enables high performance with reduced hardware complexity. An important aspect affecting the performance of such systems is the measurement matrix that is used to reduce the number of samplers. Recent studies have proposed designing CS-DOA specific measurement matrices. However, these studies assume that each sensor is connected to each sampler channel. Such designs reduce the number of analog-to-digital samplers while increasing the number of other analog components required for mixing the sensor outputs. In this study, we tackle the problem of hardware-efficient measurement matrix design for CS based DOA estimation. We first propose constructing a hardware-efficient measurement matrix by projecting the hardware-inefficient full-matrix onto block-diagonal matrices. We rigorously derive the necessary equations and provide analytical solution for the proposed projection operation. Next, we propose a structured random permutation of the sensors to maximize the similarity between the full-matrix and the block-diagonal matrix. We thoroughly validate our proposed approach by comparing it to previously proposed block-diagonal Gaussian random matrices under a variety of simulated settings.
影响因子：
4.4
作者：
Ibrahim, Mohamed;Ramireddy, Venkatesh;Thomae, Reiner S.
通讯作者：
Thomae, Reiner S.