Cooperative Approaches to Design of Nonlinear Filters

• Applicant: Professor Dr.-Ing. Uwe D. Hanebeck
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2016 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 283072193

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.

• DFG Programme: Research Grants
• International Connection: Czech Republic
• Partner Organisation: Czech Science Foundation
• Co-Investigators: Professor Dr.-Ing. Marcus Baum; Professor Dr.-Ing. Benjamin Noack
• Cooperation Partner: Professor Jindrich Dunik, Ph.D.