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 language | English (US) |
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Pages | 57-64 |
Number of pages | 8 |
DOIs | |
State | Published - 1989 |
Externally published | Yes |
Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: Jun 18 1989 → Jun 22 1989 |
Other
Other | IJCNN International Joint Conference on Neural Networks |
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City | Washington, DC, USA |
Period | 6/18/89 → 6/22/89 |
All Science Journal Classification (ASJC) codes
- General Engineering