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; 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，人们对极地中层夏季回声（PMSE）的存在背后的基本物理学已被众所周知，那里的大气湍流，带电的冰颗粒和电子起着重要作用。鉴于这种基本的理解，PMSE被用作示踪剂来研究极地高度的复杂大气动力学。具有典型大气雷达的PMSE观察很难解释，因为时间和空间特征无法分离。为了解决这些时间和空间歧义，由于所使用的系统（梁宽度，有限的接收器等）以及回声的性质，大气雷达成像（ARI）的成功程度不同。通常，回声的相关时间相对较长（几百毫秒至秒），而它们在水平漂移时。这种漂移不允许我们减少传统方法中使用的空间相关性的不确定性。通常，波束形成型算法，其中一些（包括某种类型的正则化）用于图像形成。不幸的是，这导致图像中的伪影。应对这一挑战的可能解决方案是对图像的先验知识的开发。通常，图像稀疏，只会随着时间的推移而缓慢变化。其他研究小组已经提出了压缩传感（CS）技术在ARI中的应用，但需要进一步的研究和实施。我们最近在ARI中应用了连贯的MIMO技术来研究电离层不规则。这是第一次在大气雷达中使用MIMO。将MIMO与CS结合起来提出了许多具有挑战性的研究问题，因为传感矩阵是高度结构化的。此外，CS和跟踪的组合在ARI中开辟了一个新的研究领域。首先，理论上的挑战和机会是由于测量数量可能不够大的事实，因此无法应用大型问题的现有结果和算法。特殊挑战是由于我们必须表征稀疏性（即所拥有的领域）以及没有可靠参考的时间动态的事实。解决此问题的一种可能解决方案可能是使用恢复和跟踪算法，这些算法并非专注于仅在图像重建方面做出最大的努力，但也会产生有关结果的可信度的一些信息。除了模拟外，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有力动机的模型，并进行新的实验以测试和改进我们提出的方法。除了在反转方面的帮助外，包括MIMO的包含，还可以作为测试方案，以评估无法使用MIMO的系统中所提出的方法的性能。