NDLI: New estimators for mixed stochastic and set theoretic uncertainty models: The vector case

Content Provider	IEEE Xplore Digital Library
Author	Hanebeck, U.D. Horn, J.
Copyright Year	1999
Description	Author affiliation: Corp. Technol., Inf. & Commun., Siemens AG, München, Germany (Horn, J.) \|\| Inst. of Autom. Control Eng., Tech. Univ. Munchen, München, Germany (Hanebeck, U.D.)
Abstract	This work presents new results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data fusion. A new estimator has been developed, that combines set theoretic and stochastic estimation in a rigorous manner. The estimator is efficient and, hence, well-suited for practical applications. It provides a continuous transition between the two classical estimation concepts, because it converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, the new estimator provides solution sets that are uncertain in a statistical sense.
Starting Page	1143
Ending Page	1148
File Size	278184
Page Count	6
File Format	PDF
ISBN	9783952417355
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-08-31
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	EUCA
Subject Keyword	Uncertainty Stochastic noise Mixed noise models Noise Estimation Stochastic processes Bounded noise Estimation theory Filtering techniques Approximation methods Kalman filters Vehicles
Content Type	Text
Resource Type	Article

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