论文标题
通过基于Sigma点EM的单个粒子跟踪的同时定位和参数估计
Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM
论文作者
论文摘要
单个粒子跟踪（SPT）是一种强大的工具类别，用于分析在活细胞内移动的个体生物学大分子的动力学。获得的数据通常以一系列相机图像的形式进行，然后进行后处理以揭示有关运动的详细信息。在这项工作中，我们开发了一种用于从数据共同估算粒子轨迹和运动模型参数的算法。我们的方法使用了期望最大化（EM）与无混音的卡尔曼过滤器（UKF）和无混音的朗格 - 螺丝式晶状体更光滑（URTSS），从而使我们可以使用相机获得的观察结果的准确，非线性模型。由于光子生成过程的射击噪声特性，该模型使用泊松分布来捕获成像中固有的测量噪声。为了应用UKF，我们首先必须将测量值转换为具有加性高斯噪声的模型。我们考虑两种方法，一种基于方差稳定转换（我们比较Anscombe和Freeman-Tukey Transforms），另一种基于泊松分布的高斯近似值。通过模拟，我们证明了方法的功效，并探索了这些测量转换之间的差异。
Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop an algorithm for jointly estimating both particle trajectory and motion model parameters from the data. Our approach uses Expectation Maximization (EM) combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear model of the observations acquired by the camera. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply a UKF, we first must transform the measurements into a model with additive Gaussian noise. We consider two approaches, one based on variance stabilizing transformations (where we compare the Anscombe and Freeman-Tukey transforms) and one on a Gaussian approximation to the Poisson distribution. Through simulations, we demonstrate efficacy of the approach and explore the differences among these measurement transformations.
