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 Dempater-Shafer 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.
In this paper, we propose a new structure based on photonic crystals to realise a demultiplexing operation for dense wavelength division multiplexing transmission systems. In this demultiplexer, the resonant cavities were responsible for selecting the wavelength. By imposing defect rods to these cavities, the modes could resonate at the desired frequencies. As we wanted to see the nonlinear effects, the material that was chosen for defect rods were doped glass. The refractive index of this glass in 1550 nm is 1.41. Increasing the input power causes variation in the refractive index of defect rods and as a result resonant condition of whole cavity alerts so a tenable demultiplexer can be investigated. Based on the results, the average pass bands of channels are near to 1.5 nm and the channel spacing is approximately 3.95 nm. The proposed demultiplexer acts in a near-complete transmission efficiency and the mean value of the crosstalk was −19 dB.
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