## Abstract

A parallel decision fusion system is studied where local detectors (LDs) collect information about a binary hypothesis, and transmit multi-bit intermediate decisions to a Data Fusion Center (DFC). The DFC compresses the local decisions into a final binary decision. The objective function is the Bayesian risk. Equations for the optimal decision rules for the LDs and the DFC have been derived by Lee and Chao (1989), but the computational complexity of solving them is formidable. To address this difficulty, we propose several suboptimal LD-design schemes. For each one we design a DFC, which is optimal conditioned on the fixed LD rules. We calculate the exact performance of each scheme, thus providing a means for selection of the most appropriate one under given observation conditions. We demonstrate performance for two important binary decision tasks, namely (i) discrimination between two Gaussian hypotheses of equal variances and different means; and (ii) discrimination between two Gaussian hypotheses of equal means and different variances.

Original language | English (US) |
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Pages | 207-214 |

Number of pages | 8 |

State | Published - 1994 |

Externally published | Yes |

Event | Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems - Las Vegas, NV, USA Duration: Oct 2 1994 → Oct 5 1994 |

### Other

Other | Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |
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City | Las Vegas, NV, USA |

Period | 10/2/94 → 10/5/94 |

## All Science Journal Classification (ASJC) codes

- Software
- Control and Systems Engineering