A Fast Learning Algorithm for Blind Data Fusion Using a Novel $L_{2}$ -Norm Estimation

In this paper, a novel L₂-norm estimation method for blind data fusion under noisy environments is proposed and a fast learning algorithm is developed to implement the proposed estimation method. The proposed learning algorithm is proved to be globally exponentially convergent to an optimal fusion weight vector. In addition, the proposed learning algorithm has lower computation complexity than the existing cooperative learning algorithm based a L₁-norm estimation method. Compared with other estimation methods, the proposed estimation method can be effectively used in the blind image fusion. Application examples of image fusion show that the proposed learning algorithm is able to fast obtain more accurate solutions than several conventional algorithms.