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

Time-domain processing for ocean surface parameter extraction from HF-radar data and related applications
从高频雷达数据提取海洋表面参数的时域处理及相关应用
批准号:
RGPIN-2020-07155
负责人:
Shahidi, Reza
金额:
$2.04万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

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中文摘要
翻译
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。
英文摘要
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.
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Time-domain processing for ocean surface parameter extraction from HF-radar data and related applications
  • 批准号:
    RGPIN-2020-07155
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2022
  • 负责人:
    Shahidi, Reza
  • 依托单位:
Time-domain processing for ocean surface parameter extraction from HF-radar data and related applications
  • 批准号:
    RGPIN-2020-07155
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.04万
  • 财政年份:
    2021
  • 负责人:
    Shahidi, Reza
  • 依托单位:
Time-domain processing for ocean surface parameter extraction from HF-radar data and related applications
  • 批准号:
    DGECR-2020-00455
  • 项目类别:
    Discovery Launch Supplement
  • 资助金额:
    $0.91万
  • 财政年份:
    2020
  • 负责人:
    Shahidi, Reza
  • 依托单位:
Nonlinear Partial Differential Equation-Based Methods for Image Processing
  • 批准号:
    317478-2005
  • 项目类别:
    Postgraduate Scholarships - Doctoral
  • 资助金额:
    $1.53万
  • 财政年份:
    2006
  • 负责人:
    Shahidi, Reza
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  • 项目类别:
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  • 项目类别:
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  • 项目类别:
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