NDLI: Robust fusion of location information

Content Provider	IEEE Xplore Digital Library
Author	McKendall, R. Mintz, M.
Copyright Year	1988
Description	Author affiliation: Pennsylvania Univ., Philadelphia, PA, USA (McKendall, R.; Mintz, M.)
Abstract	A sensor fusion problem for location data using statistical decision theory (SDT) is studied. The contribution of this study is the application of SDT to obtain a robust test of the hypothesis that data from different sensors is consistent and a robust procedure for combining the date which pass this preliminary consistency test. Here, robustness refers to the statistical effectiveness of the decision rules when the probability distributions of the observation noise and the a priori position information associated with the individual sensors are uncertain. Location data refers to observations of the form Z= theta +V, where V represents additive sensor noise and theta denotes the sensed parameter of interest to the observer. The paper focuses on epsilon -contamination models, which allow one to account for heavy-tailed deviations from nominal sampling distributions.
Starting Page	1239
Ending Page	1244
File Size	600676
Page Count	6
File Format	PDF
ISBN	0818608528
DOI	10.1109/ROBOT.1988.12231
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1988-04-24
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Sampling methods Testing Additive noise Sensor fusion Probability distribution Mathematical model Robot sensing systems Data engineering Systems engineering and theory
Content Type	Text
Resource Type	Article

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