中文摘要：

协作检测是认知无线电技术（CognitiveRadio）的重要组成部分，通过多认知用户协作可以提高信道衰落或者阴影效应而带来的低检测性能。传统的协作检测由于认知用户过多并且个别用户信道状况较差，因而检测性能较低。并且各用户在中心决策中都占有相同的比重，忽略了单用户的检测性能。介绍了一种采用加权系数的多簇协作检测算法，根据认知用户的信噪比配以不同的权重，以使检测概率达到最大；通过将认知用户分簇，并选择簇内信道特性最好的用户向决策中心传送信息，有效地提高了最终决策的准确度。仿真结果表明，本文算法具有较好的检测性能，与传统算法相比，能够提高检测概率和减小虚警概率。

英文摘要：

Cooperative detection is an important part in cognitive radio.Through the cooperation of some cognitive users, the per- formanee of weak detection can improve in the case of channel fading and shadow effect.Traditional cooperative detection sometimes is weak because of too many detection users and individual user＇s bad channel.Moreover, each user has the same proportion in the deci- sion of the center, and the detection performance of the single user is often ignored.A multi-cluster cooperative detection algorithm based on weighing is proposed.Through giving cognitive users different weights according to their SNR ,the detection probability can reach the maximum.And through dividing the uses into clusters and choosing the user with the best channel characteristic to report to the decision centre,the precision of final decision can be improved effectively. Simulations show that our algorithm has a better detection perform- ance, increases the detection probability, and decreases the alarm probability comparing with the traditional algorithm.