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New sequential Monte Carlo methods for nonlinear dynamic systems

机译：非线性动力学系统的新序贯蒙特卡洛方法

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摘要

In this paper we present several new sequential Monte Carlo (SMC) algorithms for online estimation (filtering) of nonlinear dynamic systems. SMC has been shown to be a powerful tool for dealing with complex dynamic systems. It sequentially generates Monte Carlo samples from a proposal distribution, adjusted by a set of importance weight with respect to a target distribution, to facilitate statistical inferences on the characteristic (state) of the system. The key to a successful implementation of SMC in complex problems is the design of an efficient proposal distribution from which the Monte Carlo samples are generated. We propose several such proposal distributions that are efficient yet easy to generate samples from. They are efficient because they tend to utilize both the information in the state process and the observations. They are all Gaussian distributions hence are easy to sample from. The central ideas of the conventional nonlinear filters, such as extended Kalman filter, unscented Kalman filter and the Gaussian quadrature filter, are used to construct these proposal distributions. The effectiveness of the proposed algorithms are demonstrated through two applications—real time target tracking and the multiuser parameter tracking in CDMA communication systems.
机译：在本文中，我们提出了几种新的顺序蒙特卡罗（SMC）算法，用于非线性动态系统的在线估计（滤波）。 SMC已被证明是处理复杂动态系统的强大工具。它从提案分布顺序生成了蒙特卡洛样本，并通过相对于目标分布的一组重要权重进行了调整，以促进对系统特征（状态）的统计推断。在复杂问题中成功实施SMC的关键是设计有效的投标分布，并从中生成蒙特卡洛样本。我们提出了一些有效但又易于从中生成样本的此类提议分布。它们之所以有效，是因为它们倾向于同时利用状态过程中的信息和观察结果。它们都是高斯分布，因此很容易采样。常规非线性滤波器（例如扩展卡尔曼滤波器，无味卡尔曼滤波器和高斯正交滤波器）的中心思想用于构造这些提议分布。通过两种应用来证明所提出算法的有效性-实时目标跟踪和CDMA通信系统中的多用户参数跟踪。

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