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As a type of main optical error source in the resonant micro-optic gyro (RMOG), the optical Kerr-effect brings a nonzero bias to the output of the RMOG. The light-intensity fluctuations are interpreted as the cause. To eliminate the drifts due to the optical Kerr-effect, the intensities of the clockwise (CW) and counterclockwise (CCW) lightwaves circulating in the resonator should be equal at all times. Through theoretical analysis and experimental investigation, a linear relationship between the second-harmonic demodulated signal and the light-intensity input to the resonator is demonstrated for the sinusoidally phase modulated RMOG. Both our numerical simulation and experimental verification are carried out, which, for the first time to the best of our knowledge, demonstrate that the second-harmonic demodulated signal can be used as a feedback error signal to reduce both the input-intensity mismatch between the CW and CCW lightwaves and their intensity fluctuations. By applying the light-intensity feedback loop to the closed-loop RMOG, the light-intensity fluctuations are reduced to $2.7\times {10}^{-5}$, down from 5.86%. As a result, the optical-Kerr effect induced error is effectively reduced.

Resonator fiber optic gyro (RFOG) based on the Sagnac effect has the potential to achieve the inertial navigation system requirement with a short sensing coil. Semiconductor laser is one of the key elements for integration and miniaturization of the RFOG. In this paper, an RFOG employing a semiconductor laser is demonstrated. The model of the laser frequency noise induced error in the RFOG is described. To attenuate the laser frequency noise induced error, active frequency stabilization is applied. An online laser frequency noise observation is built, as a powerful optimum criterion for the loop parameters. Moreover, the laser frequency noise observation method is developed as a new measurement tool. With a fast digital proportional integrator based on a single field programmable gate array applied in the active stabilization loop, the laser frequency noise is reduced to 0.021 Hz ($1\sigma$). It is equivalent to a rotation rate of $0.07°/\mathrm{h}$, and close to the shot noise limit for the RFOG. As a result, a bias stability of open-loop gyro output is $9.5°/\mathrm{h}$ ($1\sigma$) for the integration time 10 s in an hour observed in the RFOG. To the best of our knowledge, this result is the best long-term stability using the miniature semiconductor laser.

A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly. In this paper, a modified log-Polar mapping is used to constructed rotation-invariant star patterns. Then a 1D CNN is utilized to classify the star patterns associated with guide stars. In the 1D CNN model, a global average pooling layer is used to replace fully-connected layers to reduce the number of parameters and the risk of overfitting. Experiments show that the proposed algorithm is highly robust to position noise, magnitude noise, and false stars. The identification accuracy is $98.1\%$ with 5 pixels position noise, $97.4\%$ with 5 false stars, and $97.7\%$ with 0.5 Mv magnitude noise, respectively, which is significantly higher than the identification rate of the pyramid, optimized grid and modified log-polar algorithms. Moreover, the proposed algorithm guarantees a reliable star identification under dynamic conditions. The identification accuracy is $82.1\%$ with angular velocity of 10 degrees per second. Furthermore, its identification time is as short as 32.7 miliseconds and the memory required is about 1920 kilobytes. The algorithm proposed is suitable for current embedded systems.

The bias drift of a micro-electro-mechanical systems (MEMS) accelerometer suffers from the 1/f noise and the temperature effect. For massive applications, the bias drift urgently needs to be improved. Conventional methods often cannot address the 1/f noise and temperature effect in one architecture. In this paper, a combined approach on closed-loop architecture modification is proposed to minimize the bias drift. The modulated feedback approach is used to isolate the 1/f noise that exists in the conventional direct feedback approach. Then a common mode signal is created and added into the closed loop on the basis of modulated feedback architecture, to compensate for the temperature drift. With the combined approach, the bias instability is improved to less than 13 μg, and the drift of the Allan variance result is reduced to 17 μg at 100 s of the integration time. The temperature coefficient is reduced from 4.68 to 0.1 mg/°C. The combined approach could be useful for many other closed-loop accelerometers.