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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >WindSat radio-frequency interference signature and its identification over land and ocean
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WindSat radio-frequency interference signature and its identification over land and ocean

机译：WindSat射频干扰签名及其在陆地和海洋上的识别

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Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.
机译：当前，正在使用频谱差异技术来检测WindSat的星载多通道辐射计数据和高级微波扫描辐射计EOS中的射频干扰（RFI）。这种技术没有明确利用辐射计数据的多通道相关性，辐射仪数据是将RFI与自然辐射分开的关键信息。此外，由于海洋光谱差异固有的大自然变化性，对于在海洋区域观察到的辐射计数据来说，它并不是最佳的。在本文中，我们首先从通道相关性，信息内容以及WindSat和SMMR数据的主要成分方面分析了多元WindSat和扫描多通道微波辐射计（SMMR）数据。然后开发了两种基于信道相关性的陆地和海洋RFI检测方法。在陆地上，我们使用RFI指数的主成分分析（PCA）扩展了光谱差异技术，该技术将目标发射/散射特征的统计信息（通过RFI指数）和辐射计数据的多元相关性集成到PCA的单个统计框架中。在海洋上，X波段的通道回归几乎可以解释WindSat数据中的所有自然变化。因此，我们使用基于通道回归的模型差异技术直接预测无RFI的亮度温度，从而预测RFI强度。尽管最希望使用模型差异技术，但由于地面表面的异质性，因此很难在土地上应用。两种方法都可以提高我们在信道相关性方面对RFI签名的了解，并探索潜在的RFI缓解措施，从而为未来的卫星无源微波任务（例如NPOESS锥形扫描微波成像仪/ Sounder）降低了风险。新的RFI算法可有效检测陆地和海洋C波段和X波段Windsat辐射计通道中的RFI。

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