Fusion methods for multiple sensor systems with unknown error densities
type: Item
instance of: scholarly article
title: Fusion methods for multiple sensor systems with unknown error densities
zbMATH Open document ID: 0832.93057
DOI: 10.1016/0016-0032(94)90035-3
author: N. S. V. Rao
published in: Journal of the Franklin Institute
publication date: 1996-03-05T00:00:00Z
review text: The paper deals with the multiple sensor data fusion problem in the general framework of a system with multiple sensors, with unknown characteristics. The problem is generally formulated as a special case of the empirical risk minimization problem. The system is supposed to be available so that training data in the form of readings corresponding to objects of known parameters can be obtained. The aim is to estimate a fusion rule, based on the samples, which minimizes an expected square error. Suppose the probability density of the sensors to be unknown and using a suitable ``training'' algorithm, a solution method, based on the empirical risk minimization method, is proposed. It is shown that a fusion rule close to the ``optimal rule'' can be inferred under the assumptions of finite capacity for the class of fusion rules and boundedness of the errors. Some computational issues about estimating fusion rules are given. Several computational methods are determined. The sufficiency conditions for the composite system (the fuser and the sensors) to be better than the best individual sensor are identified. Based on linear approximation and nearest neighbor rule for the special case of Lipschitz continuous functions with application to feedforward neural networks, are presented. A comparison of relative performance of the composite system and individual sensors is given. The class of linearly separable systems is discussed. Further research directions are formulated.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93E25
Mathematics Subject Classification ID: 93C41
zbMATH DE Number: 799594
zbMATH Keywords: estimation and detection
zbMATH Keywords: multiple sensor data fusion
zbMATH Keywords: fusion rules
Wikidata QID: Q116677362
reviewed by: Tzvetan Semerdjiev
MaRDI profile type: Publication
full work available at URL: 0016-0032(94)90035-3
OpenAlex ID: W2030707380
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description: scientific article; zbMATH DE number 799594