Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

Dynamic Free-Spectral-Range Measurement for Fiber Resonator Based on Digital-Heterodyne Optical Phase-Locked Loop

We propose a novel scheme, based on digital-heterodyne optical phase-locked loop with whole-fiber circuit, to dynamically measure the free-spectral-range of a fiber resonator. The optical phase-locked loop is established with a differential frequency-modulation module consists of a pair of acousto-optic modulators. The resonance-tracking loop is derived with the Pound-Drever-Hall technique for locking the heterodyne frequency of the OPLL on the frequency difference between adjacent resonance modes. A stable locking accuracy of about 7 × 10?9 and a dynamic locking accuracy of about 5 × 10?8 are achieved with the FSR of 8.155 MHz, indicating a bias stability of the resonator fiber optic gyro of about 0.1?/h with 10 Hz bandwidth. In addition, the thermal drift coefficient of the FSR is measured as 0.1 Hz/?C. This shows remarkable potential for realizing advanced optical measurement systems, such as the resonant fiber optic gyro, and so on.

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.