Compressed sensing radar imaging of polar mesospheric summer echoes using tracking and MIMO approaches (CS-PMSE-MIMO)

使用跟踪和 MIMO 方法对极地中层夏季回波进行压缩感知雷达成像 (CS-PMSE-MIMO)

• 批准号: 403837627
• 负责人: Professor Dr. Jorge Chau, Ph.D.
• 金额: --
• 依托单位: Institut für Nachrichtentechnik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/403837627?language=en
• 关键词: Compressed; sensing; radar; imaging; polar

The basic physics behind the existence of polar mesospheric summer echoes (PMSE) is nowadays well understood, where atmospheric turbulence, charged ice particles and electrons play significant roles. Given this basic understanding, PMSE are being used as tracers to study the complicated atmospheric dynamics at polar mesospheric altitudes. PMSE observations with typical atmospheric radars are difficult to interpret, since temporal and spatial features can not be separated. In order to resolve these temporal and spatial ambiguities, atmospheric radar imaging (ARI) has been applied with different degrees of success, due to the systems used (beam widths, limited number of receivers, etc.), and due to the nature of the echoes. In general the echoes present relatively long correlations times (few hundreds of milliseconds to seconds) while they are horizontally drifting. Such drifting does not allow us to reduce the uncertainties on the obtained spatial correlations used in traditional methods. Usually, beamforming type algorithms, some of them including some type of regularization, are used for image formation. Unfortunately, this leads to artifacts in the image. A possible solution to this challenge is the exploitation of a priori knowledge about the image. Typically, the image is sparse and only changes slowly over time. The application of compressed sensing (CS) techniques in ARI has been proposed by other research groups but needs further investigation and implementation. We have recently applied coherent MIMO techniques in ARI to study ionospheric irregularities. This was the first time MIMO was used in atmospheric radars. Combining MIMO with CS rises many challenging research questions as the sensing matrix is highly structured. Furthermore, the combination of CS and tracking opens a new field of research in ARI. First theoretical challenges and opportunities arise from the fact that the number of measurements may not be large enough so that existing results and algorithms for large problems can not be applied. Special challenges arise from the fact that we have to characterize the sparsity, i.e., the domain in which it holds, and the time dynamics without having a reliable reference. A possible solution to this problem might be the use of recovery and tracking algorithms which do not focus on making a best effort in image reconstruction alone but also yield some information on the trustworthiness of the result. Besides simulations, we will exploit existing radar experiments to create physically motivated models for the sparsity and the time dynamics, and conduct new experiments to test and improve our proposed methods. The inclusion of MIMO, besides helping in the inversion, might serve also as test scenario to evaluate the performance of the proposed methods in systems not able to use MIMO.

如今，极地中层夏季回波（PMSE）存在背后的基本物理原理已得到充分了解，其中大气湍流、带电冰粒子和电子发挥着重要作用。鉴于这一基本认识，PMSE 被用作示踪剂来研究极地中层高度的复杂大气动力学。使用典型大气雷达进行的 PMSE 观测很难解释，因为时间和空间特征无法分开。为了解决这些时间和空间模糊性，由于所使用的系统（波束宽度、接收器数量有限等）以及由于大气雷达成像（ARI）的性质，大气雷达成像（ARI）已获得不同程度的成功。回声。一般来说，回波在水平漂移时呈现相对较长的相关时间（几百毫秒到几秒）。这种漂移不允许我们减少传统方法中使用的获得的空间相关性的不确定性。通常，波束形成类型的算法（其中一些包括某种类型的正则化）用于图像形成。不幸的是，这会导致图像中出现伪影。解决这一挑战的一个可能的解决方案是利用有关图像的先验知识。通常，图像是稀疏的，并且随着时间的推移只会缓慢变化。其他研究小组已经提出了压缩感知（CS）技术在 ARI 中的应用，但需要进一步研究和实施。我们最近在 ARI 中应用了相干 MIMO 技术来研究电离层不规则性。这是 MIMO 首次应用于大气雷达。由于传感矩阵是高度结构化的，因此将 MIMO 与 CS 相结合会引发许多具有挑战性的研究问题。此外，CS 和跟踪的结合开辟了 ARI 的新研究领域。第一个理论挑战和机遇来自这样一个事实：测量的数量可能不够大，以至于无法应用针对大型问题的现有结果和算法。由于我们必须在没有可靠参考的情况下描述稀疏性（即稀疏性所在的域）和时间动态，这一事实带来了特殊的挑战。该问题的一个可能的解决方案可能是使用恢复和跟踪算法，这些算法不仅仅专注于尽力进行图像重建，而且还产生一些有关结果可信度的信息。除了模拟之外，我们还将利用现有的雷达实验来创建稀疏性和时间动态的物理驱动模型，并进行新的实验来测试和改进我们提出的方法。 MIMO 的加入除了有助于反演之外，还可以作为测试场景来评估所提出的方法在无法使用 MIMO 的系统中的性能。