A Double Threshold Collaborative Detection Algorithm Based on Mutual Trust Degree Correction

This paper proposes a double threshold detection algorithm based on mutual trust degree correction, in order to reduce the probability of missed detection. First, the algorithm performs mutual trust correction on the local detection statistic of the information fusion center, next, performs information fusion. The algorithm reduces the influence of a single user on the detection result, which is caused by the weight distribution in the information fusion process. The algorithm makes the weight distribution in the information fusion process more reasonable and balanced. The simulation results show that the detection performance of the double threshold detection algorithm modified by the mutual trust matrix is better than that of the traditional double threshold detection algorithm.