Time-domain processing for ocean surface parameter extraction from HF-radar data and related applications

通常的处理高频雷达数据的方法是提取海洋表面参数，以及相关的应用，比如当雷达系统部署在移动的平台或船舶上时对平台运动的补偿，首先从数据中形成多普勒频谱，这涉及到将采集的数据转换到频域。虽然这种方法有一些优点，例如将一阶和高阶多普勒能量分离到频率空间的不同区域，但由于将数据转换到频域，它也会导致处理成本增加，并且可能导致结果不太准确。基于我之前在该领域的工作，本提案的研究旨在放弃通常的高频雷达数据处理的第一步，即将其转换到频域，而是直接在时域中处理数据，并在时域中获取数据。我以前工作的结果表明，这导致了概念上更简单的算法，这些算法通常比它们的频域对应物更准确，并且在实现上也更快。最近，人们发现了从移动平台获取的高频雷达数据的运动补偿问题与从x波段雷达数据对无人机（uav）进行微多普勒检测之间的联系。这两个看似完全不同的问题之间的联系将被利用，为每个问题提供新的结果和解决方案，这可能对国防和安全工业有益，因为对非合作无人机的自动检测正成为一个日益重要和紧迫的问题。高频雷达已经被用作监测海洋表面参数的一种成本较低的替代方法，比如距离海岸部署数百公里的大片海洋上的显著波高和主波方向。基于时域高频雷达算法的研究将通过降低高频雷达部署的成本和复杂性来帮助加拿大，高频雷达部署用于许多应用，如环境监测和国防。它还将允许在移动平台和船舶上更有效地部署此类系统，因为已经开发的用于从这些平台和船舶获取的数据的运动补偿的时域算法已经被证明比它们的频域对应物更准确和有效。

The usual approach to the processing of high-frequency radar data for ocean surface parameter extraction, as well as related applications, such as compensation for platform motion when the radar system is deployed on a moving platform or ship, has been to first form the Doppler spectrum from the data, which involves transforming the acquired data to the frequency domain.  Although this has some advantages, such as separating out the first and higher-order Doppler energies into different regions in frequency space, it also leads to increased processing cost and potentially less accurate results due to transformation of the data to the frequency domain. Building on my previous work in the area, the research in this proposal aims to forego the usual first step of HF radar data processing of transforming it to the frequency domain, and instead process the data directly in the time domain, where it is acquired.  Results from my previous work shows that this leads to conceptually-simpler algorithms which are often more accurate than their frequency-domain counterparts, and also faster in their implementations. Recently, links have been discovered between the problems of motion compensation of HF radar data acquired from moving platforms and Micro-Doppler detection of e.g., Unmanned Aerial Vehicles (UAVs) from X-band radar data.  Links between these two seemingly-disparate problems will be exploited to derive new results and solutions to each of them, which could potentially be of benefit to the defense and security industries, for which automatic detection of non-cooperative UAVs is becoming an increasingly important and pressing problem. High-frequency radar is already used as a lower-cost alternative to monitoring ocean surface parameters such as significant wave height and principal wave direction over large swaths of the ocean, up to several hundreds of kilometres away from their coastal deployments.  Research based on time-domain high-frequency radar algorithms will help Canada by reducing the cost and complexity of high-frequency radar deployments used for many applications, such as environmental monitoring and national defence.  It will also allow for more effective deployments of such systems on moving platforms and ships, as already-developed time-domain algorithms for motion compensation of data acquired from these platforms and ships have already been shown to be more accurate and efficient than their frequency-domain counterparts.