Decentralized Estimation and Control for Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia. Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted. Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources. Decentralized Estimation and Control for Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation. TOC:Introduction.- Background .- Motivation.- Problem Statement.- Approach.- Principal Contributions.- Book Outline.- Estimation and Information Space: Introduction. The Kalman Filter. The Information Filter. The Extended Kalman Filter (EKF). The Extended Information Filter (EIF). Examples of Estimation in Nonlinear Systems. Summary.- Decentralized Estimation for Multisensor Systems: Introduction. Multisensor Systems. Decentralized Systems. Decentralized Estimators. The Limitations of Fully Connected Decentralization. Summary.- Scalable Decentralized Estimation: Introduction. An Extended Example. The Moore-Penrose Generalized Inverse: T+. Generalized Internodal Transformation. Special Cases of Tji(k). Distributed and Decentralized Filters. Summary.- Scalable Decentralized Control: Introduction. Optimal Stochastic Control. Decentralized Multisensor Based Control. Simulation Example. Summary.- Multisensor Applications: A Wheeled Mobile Robot: Introduction. Wheeled Mobile Robot (WMR) Modeling. Decentralized WMR Control. Hardware Design and Construction. Software Development. On-Vehicle Software. Summary. Results and Performance Analysis: Introduction. System Performance Criteria. Simulation Results. WMR Experimental Results. Summary.- Conclusions and Future Research: Introduction. Summary of Contributions. Research Appraisal. Future Research Directions. Bibliography.
