Pseudo-random-bit-sequence phase modulation for reduced errors in a fiber optic gyroscope.
Low noise and drift in a laser-driven fiber optic gyroscope (FOG) are demonstrated by interrogating the sensor with a low-coherence laser. The laser coherence was reduced by broadening its optical spectrum using an external electro-optic phase modulator driven by either a sinusoidal or a pseudo-random bit sequence (PRBS) waveform. The noise reduction measured in a FOG driven by a modulated laser agrees with the calculations based on the broadened laser spectrum. Using PRBS modulation, the linewidth of a laser was broadened from 10 MHz to more than 10 GHz, leading to a measured FOG noise of only 0.00073  deg/√h and a drift of 0.023  deg/h. To the best of our knowledge, these are the lowest noise and drift reported in a laser-driven FOG, and this noise is below the requirement for the inertial navigation of aircraft.
Aircraft-navigation-grade laser-driven FOG with Gaussian-noise phase modulation.
A laser-driven fiber optic gyroscope (FOG) is demonstrated with an angular random walk noise of 5.5×10-4  deg/√h, a drift of 6.8×10-3  deg/h, and an inferred scale-factor stability of 0.15 ppm, making it, to the best of our knowledge, the first laser-driven FOG to satisfy the performance requirements for inertial navigation of commercial aircraft. This is achieved using Gaussian white noise phase modulation to broaden the linewidth of the source laser and to strongly suppress the narrow-linewidth optical carrier. The performance of this laser-driven FOG is shown to have better noise and only slightly higher drift than the same FOG driven by a conventional superfluorescent fiber source. This result is validated for two lasers with widely different intrinsic coherence.
CMEOC—An expert system in the coal mining industry
Abstract This paper introduces an expert system in the mining industry, called the Coal Mining Expert and Optimization Consultation System (CMEOC). The paper mainly describes the architecture of this system, including the knowledge base, inference engine, and relevant optimization techniques. The techniques include multi-objective programming, fuzzy sets, and integer programming. The application results present evidence of the usefulness of the system.