Fast Copula-Based Fusion of Correlated Decisions for Distributed Radar Detection

Distributed sensor networks with hundreds of antenna arrays are widely utilized for radar detection systems. When observations of network nodes are correlated, an optimal copula-based fusion (termed CBF) rule is designed for correlated binary decisions under the Neyman-Pearson (NP) framework. However, the CBF rule needs to determine the weights of all decisions in combination. Therefore, the high computational complexity of the CBF rule is unavoidable in the scenario with a large number of nodes. In this paper, we propose a fast copula-based fusion (F-CBF) algorithm for a multi-node radar system. The correlation between node decisions is simplified at the fusion center (FC) by taking logarithms of the weighted sum of node decisions. Specifically, the node decisions are weighted by a constant associated with the number of nodes. Thus the proposed method does not need to exhaust the weights of all combinations. Simulation results demonstrate that the proposed algorithm can efficiently reduce computational complexity. The detection performance of the proposed F-CBF method is significantly better than the independent detection method, although it is slightly inferior to the CBF method