A novel cooperative neural learning (CNL) algorithm based on a new linearly constrained least absolute deviation (LCLAD) method for data fusion is proposed in this paper. The state model of the proposed CNL algorithm combines adaptively three recurrent modular neural networks and is sample for implementation using both software and hardware. Unlike the conventional LAD approach, the propose LCLAD method can obtain the optimal fusion solution. Compared with the minimum variance method and linearly constrained least square method, the proposed LCLAD method can minimize an augmented least absolute deviation energy of the linearly fused information and has the robustness performance in non-Gaussian noise environments. Illustrative examples of signal and image fusion show that the quality of the solution can be more enhanced by the proposed CNL algorithm.
['Youshen Xia', 'Mohamed S. Kamel']
A Novel Cooperative Neural Learning Algorithm for Data Fusion