A sapphire fiber high temperature sensor based on Fabry-Perot interference was studied in the paper. A sapphire wafer served as the Fabry-Perot cavity was fixed to the end face of the sapphire fiber. The sensor was encapsulated in an alumina ceramic tube-ceramic sleeve structure to improve structural stability. Experiment results showed the sensor had a wide temperature measurement range of 25 °C-1550 °C. The temperature sensitivity reached 32.5 pm/°C at 1550 °C. Furthermore, the temperature was demodulated from the interference spectrum through fast Fourier transform algorithm. It was concluded that the sapphire fiber sensor with advantages of small size, good stability and a wide range of temperature measurement is very promising for measuring high temperature.

In this paper, we report an optical frequency comb (OFC) generated in a whispering-gallery-mode microbottle resonator, and present a convenient and simple method for adjustment of the zero dispersion wavelength (ZDW). The ZDW tunability of the microbottle resonator realized by exciting modes with different axial mode numbers is predicted theoretically and demonstrated experimentally by changing the coupling position of a tapered fiber. Cascaded four-wave mixing (FWM)-based frequency combs are investigated by pumping the resonator in different dispersion regimes. The results show that the OFC based on degenerated FWM could be generated under the anomalous dispersion, while Raman or Raman-assisted OFC are obtained in the normal dispersion regime.

MEMS inertial measurement unit (MIMU) has widely used in low-cost inertial navigation fields, such as pedestrian navigation system. However, the realization of self-alignment function and high-grade navigation performance are still uprising challenges. In present study, a novel MIMU/FOG compound system consisting of the MEMS inertial sensors and a skew fiber optic gyro (FOG) is proposed. The configuration which is based on the rotation modulation technology, realizes self-alignment function, and the skew FOG used as the redundant angular velocity measurement unit improves the navigation accuracy. During the rotation process, the projection of the fiber optic gyro sensitive axis is a direct current component, the earth rotation rate projected on the sensitive axis of the fiber optic gyro is a cosine component, and the initial phase is the yaw. Further, a cosine-fitting self-alignment method which requires no latitude information is introduced to calculate initial yaw alignment accuracy. The alignment time is adjustable by setting various rotation rate of rotary mechanism. When the rotary mechanism rate is fixed at 1°/s, the proposed method achieves better yaw alignment accuracy of 0.37° (1σ) by using the 0.1°/h (100s, 1σ) fiber optic gyro. Our work opens up a new perspective to implement self-alignment of the MIMU/FOG compound system.

High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

Nonlinear filtering algorithms have significant implications in the optimal estimation of navigation states and in improving the accuracy, reliability, and robustness of navigation systems. This manuscript surveys the developments of the nonlinear filtering algorithms (extended Kalman filtering (EKF), unscented Kalman filtering (UKF), Cubature Kalman filtering (CKF), particle filtering (PF), neural network filtering (NNF)) and adaptive/robust KF in integrated navigation systems. The principle, application, and existing problems of these nonlinear filtering algorithms are mainly studied, and the comparative analysis and prospect are carried out.