面向流式数字全息数据相位误差估计的动态DH-MBIR方法
Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data
AI总结：
针对定向能应用中DH相位误差估计的低延迟需求，提出动态DH-MBIR算法，通过单步迭代实现流式单次采样数据的等晕相位误差估计，大幅降低计算量与延迟，在大气湍流冻结流仿真中保持高Strehl比。
摘要：
定向能应用需要估计由大气湍流导致的数字全息（DH）相位误差，以实现出射光束的精准聚焦。这类相位误差估计必须以极低延迟完成，才能跟上大气参数的变化，因此需要通过单次DH数据采样完成相位误差估计。基于数字全息模型的迭代重建（DH-MBIR）算法能够借助期望最大化（EM）算法，通过单次采样精准估计相位误差，但现有DH-MBIR实现需要数百次迭代，无法满足实时应用需求。本文提出动态DH-MBIR（DDH-MBIR）算法，用于从流式单次采样数据中估计等晕区相位误差，延迟极低。与传统DH-MBIR相比，动态DH-MBIR算法将计算量和延迟降低了数个数量级，使应用中的实时吞吐量和延迟要求具备可行性。利用模拟大气湍流冻结流的仿真数据，我们证明该算法在每个时间步仅需1次迭代，即可在真实仿真参数下持续获得高Strehl比。
英文摘要：
Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH data. The digital holography model-based iterative reconstruction (DH-MBIR) algorithm is capable of accurately estimating phase errors in a single shot using the expectation maximization (EM) algorithm. However, existing implementations of DH-MBIR require hundreds of iterations, which is not practical for real-time applications. In this paper, we present the Dynamic DH-MBIR (DDH-MBIR) algorithm for estimating isoplanatic phase errors from streaming single-shot data with extremely low latency. The Dynamic DH-MBIR algorithm reduces the computation and latency by orders of magnitude relative to conventional DH-MBIR, making real-time throughput and latency feasible in applications. Using simulated data that models frozen flow of atmospheric turbulence, we show that our algorithm can achieve a consistently high Strehl ratio with realistic simulation parameters using only 1 iteration per timestep.
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