发表机构
Purdue University; Air Force Institute of Technology（普渡大学; 空军理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出ReVAR-ext算法，基于ReVAR扩展气动光学相位屏的空间尺寸，匹配实测数据时空相关性，实验验证其时间功率谱和空间自相关性能。
AI 中文摘要
气动光学效应会扭曲高速飞行器附近的光波传播，从而降低机载成像和通信系统的性能。通过实验测量气动光学数据成本高昂，且所得数据的空间尺寸往往有限。此外，模拟这些数据的替代方法，包括计算流体动力学和传统相位屏生成算法（例如沸腾流），面临计算时间长或统计不准确等缺点。最近，已提出数据驱动算法，能够合成与实测气动光学数据相关统计量相匹配的数据。然而，这些方法无法对气动光学数据进行空间扩展。在本文中，我们介绍了ReVAR-ext（重新白化向量自回归扩展器），一种基于现有数据驱动方法ReVAR的算法，用于空间扩展实测气动光学数据（称为外推过程），并匹配实测数据的空间和时间相关性。ReVAR-ext通过将多组合成数据与输入实测数据相结合，推广了ReVAR的生成过程。该方法生成多个固定大小的合成图像，每个图像与输入数据重叠，然后将它们拼接在一起。当与ReVAR配合使用时，ReVAR-ext算法可以生成具有任意时间持续时间和任意空间尺寸的气动光学数据。我们的实验表明，ReVAR-ext生成的扩展数据与两个实测气动光学数据集的时间功率谱紧密匹配。此外，扩展数据近似匹配空间自相关，但在大空间滞后和垂直滞后处精度有所降低。
英文摘要
Aero-optic effects distort light wave propagation near a high-speed aircraft, thereby degrading performance in airborne imaging and communication systems. Measuring aero-optic data through experiment is costly and the resulting data often has a limited spatial size. Further, alternative methods for simulating this data, including computational fluid dynamics and conventional phase screen generation algorithms (e.g., boiling flow), face drawbacks such as large computation time or inaccurate statistics. More recently, data-driven algorithms have been proposed that can synthesize data that matches relevant statistics of measured aero-optic data. However, these methods cannot spatially extend aero-optic data. In this paper, we introduce ReVAR-ext (Re-whitened Vector AutoRegression-extender), an algorithm that builds on an existing data-driven approach, ReVAR, to spatially extend measured aero-optic data (a process called outpainting) and match the spatial and temporal correlations of the measured data. ReVAR-ext generalizes the generation process of ReVAR by combining multiple sets of synthetic data with the input measured data. This approach generates multiple fixed-sized synthetic images, each of which overlaps with the input data, and then stitches them together. When paired with ReVAR, the ReVAR-ext algorithm can generate aero-optic data with arbitrary temporal duration and arbitrary spatial size. Our experiments show that extended data generated by ReVAR-ext closely matches the temporal power spectrum of two measured aero-optic data sets. Further, the extended data approximately matches the spatial autocorrelation, with reduced accuracy at large spatial lags and at vertical lags.
Comments14 pages, 7 figures. To be published in Proc. SPIE: Unconventional Imaging, Sensing, and Adaptive Optics (2026)
