Novel pentagonal photonic crystal fiber with high birefringence, large flattened negative dispersion, and high nonlinearity is proposed. The dispersion and birefringence properties of this structure are simulated and analyzed numerically based on the full vector finite element method (FEM). Numerical results indicate that the fiber obtains a large average dispersion of $-611.9\mathrm{ps}/\mathrm{nm}/\mathrm{km}$ over 1,460–1,625 nm and $-474\mathrm{ps}/\mathrm{nm}/\mathrm{km}$ over 1425–1675 nm wavelength bands for two kinds of optimized designs, respectively. In addition, the proposed PCF shows a high birefringence of $1.67\times {10}^{-2}$ and $1.75\times {10}^{-2}$ at the operating wavelength of 1550 nm. Moreover, the influence of the possible variation in the parameters during the fabrication process on the dispersion and birefringence properties is studied. The proposed PCF would have important applications in polarization maintaining transmission systems, residual dispersion compensation, supercontinuum generation, and the design of widely tunable wavelength converters based on four-wave mixing.

In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method’s computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.

We present an investigation of bias error caused by the Faraday effect in interferometric fiber optical gyroscopes (IFOGs) with double sensitivity. Jones matrix method is utilized to formulize the bias error in IFOGs with double sensitivity under the action of radial magnetic field. Theoretical results show that the bias error caused by the Faraday effect can be suppressed effectively in IFOGs with double sensitivity. Experiments with different lengths and twists of fiber coils are performed, and the experimental results support these theoretical predictions. For 490- and 1020-m polarization maintaining fiber coils, the maximum errors generated by a 1-mT radial magnetic field are 0.8°/h and 0.5°/h, respectively. This letter is promising for improving the bias stability of IFOGs.

Improving the performance of interferometric fiber optic gyroscope (IFOG) in harsh environments, such as magnetic field and temperature field variation, is necessary for its practical applications. This paper presents an investigation of Faraday effect-induced bias error of IFOG under varying temperature. Jones matrix method is utilized to formulize the temperature dependence of Faraday effect-induced bias error. Theoretical results show that the Faraday effect-induced bias error changes with the temperature in the non-skeleton polarization maintaining (PM) fiber coil. This phenomenon is caused by the temperature dependence of linear birefringence and Verdet constant of PM fiber. Particularly, Faraday effect-induced bias errors of two polarizations always have opposite signs that can be compensated optically regardless of the changes of the temperature. Two experiments with a 1000 m non-skeleton PM fiber coil are performed, and the experimental results support these theoretical predictions. This study is promising for improving the bias stability of IFOG.