Optimizing for Spatial Frequency Coverage vs. Point-Spread Function Sidelobe Level in Active Incoherent Microwave Imaging Arrays

Sean M. Ellison, Stavros Vakalis, J. Nanzer
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Abstract

Array optimization to maximize image reconstruction performance is often approached using numerical methods due to the solution space being too large for traditional brute force methods. In this work, a sixteen platform coherent distributed array with a seven element subarray attached to each platform will be optimized in two separate domains, each using a genetic algorithm: one to optimize spatial frequency content and the other to optimize peak to sidelobe level of the point spread function. The dimension of the array solution space is restricted to a planar domain of 50λ × 50λ, and the image reconstruction performance is compared for the outputs of both optimization approaches. It is demonstrated that optimizing the point spread function results in better reconstruction of images containing typical spatial frequency distributions, while optimizing the spatial frequency coverage results in better reconstruction of images containing mostly high-spatial frequency content, such as images consisting mostly of shape outlines.
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有源非相干微波成像阵列空间频率覆盖与点扩展函数旁瓣电平优化
由于传统的暴力破解方法的求解空间太大,为了使图像重建性能最大化,通常采用数值方法进行阵列优化。在这项工作中,一个16个平台的相干分布式阵列,每个平台附加一个七元子阵列,将在两个单独的域中进行优化,每个域使用遗传算法:一个优化空间频率内容,另一个优化点传播函数的峰值到旁瓣电平。将阵列解空间的维数限制在50λ × 50λ的平面域内,比较了两种优化方法输出的图像重建性能。结果表明,优化点扩展函数可以更好地重建包含典型空间频率分布的图像,而优化空间频率覆盖可以更好地重建包含高空间频率内容的图像,例如主要由形状轮廓组成的图像。
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