## Abstract

Design of parallel binary decision fusion systems is often performed under the assumption that the decision integrator (the data fusion center, DFC) possesses perfect knowledge of the local-detector (LD) statistics. In most studies, other statistical parameters are also assumed to be known, namely the a priori probabilities of the hypotheses, and the transition probabilities of the DFC-LD channels. The local observations are assumed to be statistically independent (conditioned on the hypothesis). Under these circumstances, the DFC's sufficient statistic is a weighted sum of the local decisions and the weights depend on the statistical parameters. When these parameters are unknown, we propose to estimate them on-line, using the discriminant function of the DFC as the performance index. This process can be performed with the help of a teacher that supplies the correct labels of the data, or without supervision, by estimating the correct labels from the data. The label-estimates are generated by the DFC, on the basis of its own past decisions and its assessment of the LD-data reliability.

Original language | English (US) |
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Pages (from-to) | 5004-5009 |

Number of pages | 6 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

Volume | 5 |

State | Published - Dec 1 1997 |

Externally published | Yes |

Event | Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA Duration: Dec 10 1997 → Dec 12 1997 |

## All Science Journal Classification (ASJC) codes

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization