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SAR and MTI processing of sparse satellite clusters.

机译:稀疏卫星簇的SAR和MTI处理。

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The concept of radar satellite constellations has been proposed in the literature and is currently under research. These constellations possess the wide angular coverage necessary for performing spaceborne MTI and the spatial sampling necessary for wide-area SAR. However, the constellations form arrays that are sparsely populated, three-dimensional, and irregularly spaced. Therefore, current SAR and MTI processing algorithms, which often assume a single element or a uniform, linear array, are not well suited to this type of implementation.; In order to derive effective SAR and MTI processing techniques, I develop a novel method of characterizing the behavior of sparse-array radar. A radar's five sensor parameters-time, frequency, and 3D spatial location-are equivalently represented by a 2D synthetic aperture. This synthetic aperture is then used to characterize radar properties such as resolution and ambiguity. In addition, I use the synthetic aperture as the basis for a new method of estimating clutter rank when performing MTI. The synthetic aperture is crucial to accurately estimating clutter rank using this technique.; From the synthetic aperture, effective SAR and MTI algorithms are developed. It becomes apparent that traditional SAR matched filtering produces poor results due to the sidelobes of the sparse, irregular array; therefore, other filters are derived to handle the sparse-array case. Simulations are performed for each filtering technique, the performance of the filters versus various system parameters is compared, and impacts of the constellation concept on system design are inferred from the results.; Next, the synthetic aperture is used to estimate clutter rank and define a clutter subspace. MTI is then performed through generalized DPCA filtering, which projects the radar data orthogonal to the predicted clutter subspace. Although DPCA is a mature concept, application of DPCA to a sparse, spaceborne array is new. MTI performance is also analyzed versus system parameters, and conclusions are made concerning the design limitations on sparse, spaceborne MTI systems.
机译:雷达卫星星座的概念已在文献中提出,目前正在研究中。这些星座具有执行星载MTI所需的广角覆盖范围和广域SAR所需的空间采样。但是,这些星座形成了人口稀疏,三维且空间不规则的阵列。因此,当前的SAR和MTI处理算法通常采用单个元素或统一的线性阵列,因此不太适合此类实现。为了得出有效的SAR和MTI处理技术,我开发了一种表征稀疏阵列雷达行为的新颖方法。雷达的五个传感器参数(时间,频率和3D空间位置)等效地由2D合成孔径表示。然后,该合成孔径用于表征雷达特性,例如分辨率和模糊度。另外,我使用合成孔径作为执行MTI时估算杂波等级的新方法的基础。合成孔径对于使用此技术准确估计杂波等级至关重要。从合成孔径出发,开发了有效的SAR和MTI算法。显然,由于稀疏,不规则阵列的旁瓣,传统的SAR匹配滤波产生的效果很差。因此,派生其他过滤器来处理稀疏数组的情况。对每种滤波技术都进行了仿真,比较了滤波器与各种系统参数的性能,并从结果推断出星座概念对系统设计的影响。接下来,合成孔径用于估计杂波等级并定义杂波子空间。然后,通过广义DPCA滤波执行MTI,该滤波将与预测的杂波子空间正交的雷达数据投影出来。尽管DPCA是一个成熟的概念,但将DPCA应用于稀疏的星载阵列是新的。还分析了MTI性能与系统参数之间的关系,并得出了有关稀疏星载MTI系统设计局限性的结论。

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