Investigation of defective hybrid cladding with silicon nanocrystal PCF for supercontinuum generation

Defective hybrid cladding through a silicon nanocrystal-core-structured photonic crystal fiber intended for high pump power supercontinuum proliferation is discussed in this paper. The cladding comprehends a hybrid approach of a hexagonal air hole in the outer section and a petal-structured air hole in the inner layer with a twisted pattern. Such a procedure with an air hole in the cladding section with a silicon nanocore displays high nonlinearity and negative dispersion at the communication window for varying pulse widths with 20 kW pump power. The impact of structural parameters of the proposed structure on the optical constraints is discussed, namely, dispersion, nonlinearity and group-velocity dispersion for wavelengths ranging from 0.45 µm to 1.85 µm. The proposed structure with optimized structural parameters provides high nonlinearity of about 6.38 × 106 W−1 km−1 with negative dispersion of −70.19 ps (nm km)−1 at 1550 nm.

PCF Design With Extremely High Nonlinearity And Extremely Negative Dispersion

In this manuscript we designed a circular photonic crystal fiber (PCF) having three rectangular holes filled with GaP in the core region, three air hole rings and one annular air ring in cladding region. We found highest negative dispersion for the 1.8µm pitch alongwith very low confinement loss at wavelength 1.55µm. This designed PCF offers high nonlinearity (39612 W-1km-1) and high negative dispersion (-6586 ps nm-1 km-1) alongwith zero confinement loss at 1.55µm wavelength. We also compared the proposed PCF with the previously published PCF structure and found that the nonlinearity and negative dispersion of the designed PCF are very high in comparison to circular air hole based PCF. Another performance parameters viz. birefringence, numerical aperture, effective area and effective material loss are also analyzed.

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.