Application of wavelet package filtering in the de-noising of fiber optic gyroscopes

To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and semi-soft threshold filtering is chosen based on the comparison of hard threshold and soft threshold filtering. The semi-soft threshold wavelet package filtering method is applied in the filtering of the FOG output signal. Experiments of the stationary and dynamic FOG output signals filtered with the wavelet package analysis are carried out in a lab environment, respectively. Experiments done with the real-time measured FOG signal show that the method of semi-soft threshold wavelet package filtering reduces the mean square error from 5 （°）/h to 1 （°）/h, so it is effective in eliminating the white noises and the fractal noises existing in the FOG. The novel method proposed here is proved valid in reducing the FOG drift error, satisfying the technical demands of high precision and realtime processing.

Residual intensity modulation in resonator fiber optic gyros with sinusoidal wave phase modulation

We present how residual intensity modulation(RIM) affects the performance of a resonator fiber optic gyro(R-FOG) through a sinusoidal wave phase modulation technique. The expression for the R-FOG system's demodulation curve under RIM is obtained. Through numerical simulation with different RIM coefficients and modulation frequencies, we find that a zero deviation is induced by the RIM effect on the demodulation curve, and this zero deviation varies with the RIM coefficient and modulation frequency. The expression for the system error due to this zero deviation is derived. Simulation results show that the RIM-induced error varies with the RIM coefficient and modulation frequency. There also exists optimum values for the RIM coefficient and modulation frequency to totally eliminate the RIM-induced error, and the error increases as the RIM coefficient or modulation frequency deviates from its optimum value; however, in practical situations, these two parameters would not be exactly fixed but fluctuate from their respective optimum values, and a large system error is induced even if there exists a very small deviation of these two critical parameters from their optimum values. Simulation results indicate that the RIM-induced error should be considered when designing and evaluating an R-FOG system.

An Open-Loop Test of a Resonator Fiber Optic Gyro

The resonator fiber optic gyro （R-FOG） ,which utilizes a resonance frequency change due to the Sagnac effect,is a promising candidate for the next generation inertial rotation sensor. In this study, an open-loop R-FOG is set up using phase modulation spectroscopy. First,the demodulation curve is obtained using a lock-in amplifier. From the demodulation signal,a gyro dynamic range of ± 4.2rad/s is obtained. Then,using different phase modulation frequencies,the open-loop gyro output signal is measured when the gyro is rotated clockwise or counterclockwise. The bias drift as a function of time is also measured. The fluctuation of the output over 5s is about 0.02rad/s. The drift can be reduced by taking countermeasures against system noise.

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.