Evaluation of Data Fusion in Radars Network and Determination of Optimum Algorithm

Functionality of radars network strongly depends on data fusion algorithms. Because of ambiguous in radar backscatter, probability of detection is an important parameter in choosing optimized algorithm.Radar gating and swerling of targets are two fundamental parameters for probability of detection. In this paper, three custom data fusion algorithms, Averaging, Bayesian and Dempster-Shafer are simulated. Results are compared by simulated radar input data, and evaluated by convergence, precious, influence of fluctuations, running time and complexity of implementation. Results of evaluation declare DempaterShafer algorithm is optimized for two-cell network. In four-cell network, if radars outputs are mass functions directly, hierarchical topology with Dempater-Shafer algorithm in both layers will be optimize. When radars outputs are probability, because of pignistic transform in radar output and inverse pignistic transform in radar input, hierarchical topology with Average algorithm in first layer and Bayesian algorithm in second layer will be optimize