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Online Model-Based Estimation for Automated Optical System Alignment and Phase Retrieval Algorithm

機(jī)譯：基于在線模型的光學(xué)系統(tǒng)自動(dòng)對準(zhǔn)和相位提取算法估計(jì)

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Online model-based estimation is applied to two major applications in optics: Automated optical component alignment and wavefront reconstruction with simultaneous system parameter estimation. Both applications utilize mechanical perturbation in the optical system to generate phase diversity in real-time stochastic systems.;The first part of this study proposes a novel automated alignment method which improves efficiency and increases the flexibility of an optical system. Current optical systems with automated alignment capabilities are typically designed to include a dedicated wavefront sensor. Here, we demonstrate a self-aligning method for a reconfigurable system using only focal plane images. We define reconfigurable and reflective optical systems and simulate the images given misalignment parameters using ZEMAX software. We perform a principal component analysis (PCA) on the simulated dataset to obtain Karhunen-Loeve (KL) modes, which form the basis set whose weights are the system measurements. A model function which maps the state to the measurement is learned using nonlinear least squares fitting and serves as the measurement function for the extended Kalman filter (EKF) and unscented Kalman filter (UKF) used to estimate the state and control the system. The observability and stability of the system are discussed. We present both simulated and experimental results of the full system in operation.;The second part of this study presents a novel algorithm for phase retrieval and optical system parameter estimation. Many wavefront reconstruction techniques estimate the amplitude and phase from multiple intensity measurements. One can generate phase diversity among these intensity measurements by varying certain parameters in the optical system. These parameters are subject to noise and disturbances, which might strongly degrade the accuracy of the reconstruction. The parallel algorithm iterative amplitude and phase retrieval (APR) have been proven to accurately reconstruct arbitrary wavefronts from multiple intensity measurements when system parameters are known exactly, given the ability to induce phase diversity between images. Such sets of intensity images with phase diversity can be generated by moving a lens in the optical system, but any position error on the lens will degenerate the reconstruction result. We demonstrate the use of an expectation-maximization (EM) algorithm with Kalman smoothing for recovering both the complex field and the lens position from a stack of intensity images. Our method successfully reduces the mean-squared-error of the estimated wavefront in comparison to an approach without position error estimation. We present and discuss the results of using a Kalman smoother and nonlinear least-square optimization for the estimation of the moving lens position.;We modify and extend the system variable estimation method to serial phase retrieval algorithm. We present the use of iterated extended Kalman filter (IEKF) to estimate the system variables in a multiple-image phase retrieval framework. An iterated extended Kalman filter is shown to effectively reduce the normalized mean-square-error of the reconstructed wavefront by estimating the defocus and transverse shifts of a moving camera in simulation. Experiments are conducted using two different test objects, and the results clearly demonstrate the enhancement of detail and contrast of the wavefront when using the filter. A quadratic phase introduced by a convex lens is used with a binary mask as one of the test objects. The focal length estimated from the unwrapped phase agrees with the (+/-1% tolerance) value provided by the manufacturer.
機(jī)譯：基于在線模型的估計(jì)被應(yīng)用于光學(xué)的兩個(gè)主要應(yīng)用中：自動(dòng)光學(xué)組件對準(zhǔn)和具有同時(shí)系統(tǒng)參數(shù)估計(jì)的波前重建。兩種應(yīng)用都利用光學(xué)系統(tǒng)中的機(jī)械擾動(dòng)在實(shí)時(shí)隨機(jī)系統(tǒng)中產(chǎn)生相位分集。本研究的第一部分提出了一種新穎的自動(dòng)對準(zhǔn)方法，該方法可以提高效率并增加光學(xué)系統(tǒng)的靈活性。具有自動(dòng)對準(zhǔn)功能的當(dāng)前光學(xué)系統(tǒng)通常被設(shè)計(jì)為包括專用的波前傳感器。在這里，我們演示了僅使用焦平面圖像的可重配置系統(tǒng)的自對準(zhǔn)方法。我們定義可重新配置和反射的光學(xué)系統(tǒng)，并使用ZEMAX軟件模擬給定失準(zhǔn)參數(shù)的圖像。我們對模擬數(shù)據(jù)集執(zhí)行主成分分析（PCA），以獲得Karhunen-Loeve（KL）模式，這些模式形成權(quán)重為系統(tǒng)度量的基礎(chǔ)集。使用非線性最小二乘擬合學(xué)習(xí)將狀態(tài)映射到測量的模型函數(shù)，并將其用作用于估計(jì)狀態(tài)并控制系統(tǒng)的擴(kuò)展卡爾曼濾波器（EKF）和無味卡爾曼濾波器（UKF）的測量函數(shù)。討論了系統(tǒng)的可觀察性和穩(wěn)定性。我們給出了整個(gè)系統(tǒng)在運(yùn)行中的仿真結(jié)果和實(shí)驗(yàn)結(jié)果。；第二部分研究提出了一種新的相位檢索和光學(xué)系統(tǒng)參數(shù)估計(jì)算法。許多波前重建技術(shù)可通過多次強(qiáng)度測量來估算振幅和相位。通過改變光學(xué)系??統(tǒng)中的某些參數(shù)，可以在這些強(qiáng)度測量之間產(chǎn)生相位分集。這些參數(shù)易受噪聲和干擾的影響，這可能會(huì)嚴(yán)重降低重建的準(zhǔn)確性。并行算法迭代幅度和相位檢索（APR）已被證明可以準(zhǔn)確地從系統(tǒng)已知的多個(gè)參數(shù)測量中，從多重強(qiáng)度測量中準(zhǔn)確重建任意波前，并具有在圖像之間引起相位差異的能力?？梢酝ㄟ^在光學(xué)系統(tǒng)中移動(dòng)透鏡來生成具有相位分集的這種強(qiáng)度圖像集，但是透鏡上的任何位置誤差都會(huì)使重建結(jié)果退化。我們演示了結(jié)合卡爾曼平滑技術(shù)使用期望最大化（EM）算法從強(qiáng)度圖像堆棧中恢復(fù)復(fù)數(shù)場和晶狀體位置的方法。與沒有位置誤差估計(jì)的方法相比，我們的方法成功地減小了估計(jì)波前的均方誤差。我們提出并討論了使用Kalman平滑器和非線性最小二乘法優(yōu)化來估計(jì)移動(dòng)鏡頭位置的結(jié)果。；我們將系統(tǒng)變量估計(jì)方法進(jìn)行了修改并將其擴(kuò)展到串行相位檢索算法。我們目前使用迭代擴(kuò)展卡爾曼濾波器（IEKF）來估計(jì)多圖像相位檢索框架中的系統(tǒng)變量。迭代的擴(kuò)展卡爾曼濾波器通過在仿真中估計(jì)運(yùn)動(dòng)相機(jī)的散焦和橫向偏移，可以有效地減少重構(gòu)波前的歸一化均方誤差。使用兩個(gè)不同的測試對象進(jìn)行了實(shí)驗(yàn)，結(jié)果清楚地證明了使用濾波器時(shí)波前細(xì)節(jié)和對比度的增強(qiáng)。由凸透鏡引入的二次相與二元掩模一起用作測試對象之一。從展開階段估計(jì)的焦距與制造商提供的（+/- 1％公差）值一致。

著錄項(xiàng)

  • 作者

    Fang, Joyce.;

  • 作者單位

    Cornell University.;

  • 授予單位 Cornell University.;
  • 學(xué)科 Engineering.;Mechanical engineering.
  • 學(xué)位 Ph.D.
  • 年度 2018
  • 頁碼 163 p.
  • 總頁數(shù) 163
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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