Theory of generalized particle filtering

广义粒子过滤理论

• 批准号: 0515246
• 负责人: Petar Djuric
• 金额: --
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-15 至 2010-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515246&HistoricalAwards=false
• 关键词: Theory; generalized; particle; filtering

In the past decade, particle filtering has generated astounding interest among engineers and scientists with its capacity to process data that are modeled by dynamic systems. These methods belong to the family of procedures for sequential signal processing where the objectives are to filter, predict, or smooth unknown and time-varying signals from available observations. The general area of work in this research effort is the building of a new class of particle filters, the development of their theory, and their application to a number of important tasks.The known particle filtering methods require a mathematical representation of the system dynamics and assumptions about the state transition probability distribution function, and the likelihood of the states. These probabilistic assumptions are often inaccurate and made out of convenience, and in many cases lead to formidable degradations in performance of the particle filters. We develop a more general class of particle filters which do not use probabilistic model assumptions. Instead, the new filters are based on discrete measures defined by particle streams and associated costs that are sequentially updated. With the developed theory, we are able to build particle filters that are simpler, more accurate, more robust, and more flexible than the conventional ones. The standard particle filters, however, are particular instances of the new filters. We investigate in great detail various important issues including the foundations of the new filters, their convergence, connections of the new theory with existing theories, and its extensions to batch type signal processing. The filters are tested on various challenging problems.

在过去的十年中，粒子滤波因其处理动态系统建模数据的能力而引起了工程师和科学家的极大兴趣。这些方法属于顺序信号处理过程系列，其目标是从可用观测中过滤、预测或平滑未知且随时间变化的信号。这项研究工作的一般工作领域是构建一类新型粒子滤波器、发展其理论以及将其应用于许多重要任务。已知的粒子滤波方法需要系统动力学的数学表示以及关于状态转移概率分布函数和状态可能性的假设。 这些概率假设通常是不准确的，并且是出于方便而做出的，并且在许多情况下会导致粒子过滤器性能的严重下降。我们开发了一类更通用的粒子滤波器，它不使用概率模型假设。相反，新的过滤器基于由粒子流定义的离散测量以及顺序更新的相关成本。借助先进的理论，我们能够构建比传统粒子过滤器更简单、更准确、更稳健、更灵活的粒子过滤器。然而，标准粒子过滤器是新过滤器的特殊实例。我们详细研究了各种重要问题，包括新滤波器的基础、它们的收敛性、新理论与现有理论的联系及其对批处理类型信号处理的扩展。这些过滤器针对各种具有挑战性的问题进行了测试。