Cooperative Approaches to Design of Nonlinear Filters

本项目致力于基于噪声测量的(非线性)离散时间随机动态系统的状态估计。状态估计在许多应用中起着关键作用，在这些应用中，例如(多步骤)预测、控制、故障检测或一般用于决策需要系统状态的知识。在机器人和自动化领域，导航、跟踪、定位和最优控制等领域有着重要的应用。在过去的几十年里，非线性滤波器的设计引起了文献的极大关注，并提出了许多在假设、估计质量、效率和目的方面不同的(近似)滤波方法。绝大多数建议的过滤器都不是最优的，因此是针对特定设置量身定做的。因此，为应用程序选择合适的过滤器是一项具有挑战性的任务。通常情况下，需要进行大量的仿真，不同的非线性滤波器相互竞争。本项目的目标是从竞争使用非线性滤波器转变为合作使用。特别是，重点设计了一种概念上新的非线性滤波器设计的合作方法的一般框架。在某种意义上，这种方法利用了各种非线性--通常是近似--滤波器的互补特性的组合。该项目分析了四种可能的本地过滤器的合作行为(集成、监控、组合和反馈)，并开发了各自的算法。在滤波器设计中同时使用多个协作类型可以得到一个协作的全局滤波器。与传统的具有竞争性的滤波器设计相比，协作式全局滤波器在精度、可靠性和完整性方面将提供总体上更高的估计质量。

This project is devoted to the state estimation of (nonlinear) discrete-time stochastic dynamic systems based on noisy measurements. State estimation plays a key role in many applications where the knowledge of a system state is required for, e.g., (multistep) prediction, control, fault detection, or generally for a decision making. Important applications such as navigation, tracking, localization, and optimal control can be found in the areas of robotics and automation.In the past decades, the design of nonlinear filters has attracted a significant attention in literature and many (approximate) filtering approaches differing in assumptions, estimation quality, efficiency, and purpose have been proposed. The vast majority of the proposed filters are not optimal; and hence, tailored to specific settings. As a consequence, it is a challenging task to select an appropriate filter for an application. Typically, extensive simulations have to be performed in which different nonlinear filters compete against each other.The objective of this project is to shift from a competitive use of nonlinear filters to a cooperative approach. In particular, the stress is laid on the design of a general framework for a conceptually new cooperative approach to nonlinear filter design. This approach takes an advantage of a combination of, in some sense, complementary properties of various nonlinear - usually approximate - filters. The project analyzes four types of possible cooperative behavior of local filters (integration, monitoring, combination, and feedback) and develops respective algorithms. Using several cooperation types in filter design simultaneously leads to a cooperative global filter. Compared to the traditional filter design, which is rather competitive, the cooperative global filter will provide estimates with generally higher estimation quality in terms of accuracy, credibility, and integrity.