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Proceedings Paper

Unified nonparametric data fusion
Author(s): Ronald P. S. Mahler
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Paper Abstract

In several recent papers we demonstrated that classical single-sensor, single-source statistics can be directly extended to the multisensor, multisource case. The basis for this generalization is the finite random set, together with a set of direct parallels between random-set and random- vector theories which allow familiar statistical techniques to be directly transferred to data fusion problems. We previously showed that parametric point estimation theory can be thus generalized, resulting in fully integrated data fusion algorithms. However, parametric estimation is not appropriate when sensor noise distributions are poorly known. Also, since most data fusion algorithms are partially ad hoc constructions it is difficult to determine the overall statistical behavior of such algorithms even if the statistics of the sensors are well understood. This paper shows how a standard nonparametric estimation technique, the projection kernel approach to estimating unknown probability distributions, can be extended directly to the data fusion realm.

Paper Details

Date Published: 5 July 1995
PDF: 9 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213008
Show Author Affiliations
Ronald P. S. Mahler, Unisys Corp. (United States)

Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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