中文摘要：

使用基于改进型人工免疫策略的优化算法，在统计相关的观测条件下，对固定融合规则的基于奈曼-皮尔逊准则的分布式多传感器决策融合系统进行优化设计。算法首先以筛选算子进行预搜索缩小范围，然后使用人工免疫策略方法进行全局搜索，计算过程无须使用目标函数的导数信息。对分布式多传感器决策融合系统的优化设计结果表明，优化算法在收敛性和精度上均优于传统梯度算法，并在此基础上对不同信号期望值下的最优融合规则进行了讨论。

英文摘要：

A method based on an improved artificial immune strategy is introduced for the optimization of distributed multi-sensor decision fusion systems under Neyman-Pearson criteria for the cases with statistically dependent observation and fixed fusion rule. The object function is optimized in two steps without any information of its derivation： filter operator is used for pre-search to reduce the search space and then an artificial immune strategy is applied for the global search. The experimental results show that the proposed method has better convergence and higher precision than the traditional gradient algorithms. A further discussion on the best fusion rule for different means of signals is given.