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From the Publisher:This work presents a concise treatment of robust estimation, with a thorough presentation of Kalman filtering. The robust game theoretic/H[subscript [infinity]] filtering theory is developed, making it possible to design estimators that are more general than Kalman filters and are robust to model uncertainties and rapid model variations. It also reviews the likelihood ratio method for failure detection and demonstrates how robust filters can enhance such methods by enabling the design of failure detectors that are sensitive to failures but insensitive to model uncertainties and/or rapid model variations. Robust Estimation and Failure Detection is of particular value to students, researchers and engineers with an interest in filtering or failure detection, offering classical and advanced theories and design methods and allowing them to benefit from the robust control theoretic developments of the last fifteen years. Control researchers and engineers will also find it relevant, as it demonstrates how development in their discipline affects these two neighbouring fields.