NDLI: Optimal segmentation by random process fusion

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Proceedings of the third international conference on information fusion

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Adaptive track fusion in a multisensor environment

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Optimal segmentation by random process fusion

Content Provider	IEEE Xplore Digital Library
Author	Reboul, S. Brige, D.
Copyright Year	2000
Description	Author affiliation: Lab. d'Anal. des Syst., Univ. du Littoral, Calais, France (Reboul, S.; Brige, D.)
Abstract	We introduce in this article an optimal segmentation method of nonstationary random processes. Segmentation of a non stationary process consists in assuming piecewise stationarity and in detecting the instants of change. We consider here that all the data from all the sensors are available in a same rime and perform a global segmentation. The bayesian fusion method we propose for the segmentation is based on the introduction of a joint prior model for the simultaneously segmentation and estimation of data coming from a set of sensors. We build a change process and define its prior distribution for the data fusion. That allows us to propose the MAP estimate as well as some minimum contrast estimate as a solution. We define, in the parametric processes distribution case, the expression and signification of all the segmentation's parameters. We compare the performance of our detection method in the case of two or three sensor. Application to the fusion of wind data velocity and direction is proposed.
File Size	390774
File Format	PDF
ISBN	2725700000
DOI	10.1109/IFIC.2000.862651
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-10
Access Restriction	Subscribed
Rights Holder	Int. Soc. Inf. Fusion
Subject Keyword	Bayesian methods Random processes Sensor fusion Scattering parameters Wind speed Medical signal detection Nondestructive testing Signal processing Biomedical imaging Surveillance
Content Type	Text
Resource Type	Article

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