多层并行决策融合的贝叶斯方法

目的 研究多层并行决策融合系统的决策规则并探讨该决策规则的可行性，方法 利用贝叶斯最小风险准则给出各层融合单元的决策规则，选取不同的损失函数，讨论它们对系统性能的影响。结果 损失函数中虚警损失和漏损失的比值不同导致整个系统的性能不同，与Ｎ－Ｐ准则相比较，贝叶斯方法能够充分利用先验知识和样本知识，结论 所给出的决策规则使性能更完善且该决策规则的门限容易计算，在处理决策融合的实际问题中有很大的灵活性

Bayesian Method and Its Application to Multiple Level Decision Fusion with Distributed Sensors

Aim To study the decision rules in the structure of multiple level decision fusion and examine the feasibility of these decision rules. Methods The decision rules for fusion units in every level were provided by use of the minim- um Bayesian risk criterion. Different loss functions were used to discuss their effect on the system performance. Results Different ratios of the loss of false alarm to the loss of non- detection resulting in different system performance. Compared to N- P criterion, Bayesian method can make use of the prior knowledge and the sample knowledge. Conclusion The system performance become more sound and it is easier to calculate the thresholds of the decison rules. Bayesian method has great flexibility in dealing with the practical problems about decision fusion.