Adaptive sensor fusion with nets of binary threshold elements

Moshe Kam, Ari Naim, Paul Labonski, Allon Guez

Research output: Contribution to conferencePaperpeer-review

Abstract

A simple distributed-detection scheme whose probability of error can be calculated analytically is demonstrated, and it is shown that it corresponds to a two-layer network of binary threshold elements. The authors assume that the sensors and the fusion center are subject to sudden unpredictable changes in the environment that they survey and show how learning algorithms can be used in order to maintain good performance, in spite of these changes. They conclude with an example involving five unequal sensors which distinguish between two time-varying Gaussian populations of different means.

Original languageEnglish (US)
Pages57-64
Number of pages8
StatePublished - Dec 1 1989
Externally publishedYes
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Adaptive sensor fusion with nets of binary threshold elements'. Together they form a unique fingerprint.

Cite this