In the field of high-precision intelligent optical remote sensing satellites, in order to accurately measure the temperature and vibration changes of the core components inside the optical remote sensing system, a high-precision intelligent optical remote sensing measurement system based on distributed fiber optic measurement sensors is designed, which meets the accuracy measurement requirements under thermal vacuum environment assessment and solves problems such as blurred in orbit images, insufficient positioning accuracy, low compensation efficiency, and difficult post-processing of high-precision cameras. It promoted health monitoring and intelligent adjustment of satellite in orbit images.
In a serial wavelength division multiplexing (WDM) fiber Bragg grating (FBG) sensor network, it is well known that there are challenges in separating overlapping signals, which require high precision and low delays. And using an optical spectrum analyzer as a data source result in demodulation models that are impractical for use in engineering applications. Therefore, an overlapping spectral demodulation model based on transfer learning using a charge-coupled device (CCD) interrogator and light gated recurrent unit (Li-GRU) neural networks is proposed. This model can achieve a low signal demodulation error, even when applied to data collected using a CCD interrogator with low spectral resolution and a high signal-to-noise ratio. We describe the operation principle of the Li-GRU neural network and discuss the impact of transfer learning and CNN feature extraction layers on demodulation performance. The experimental results show that lowest root mean square error of our proposed model is 1.93 pm, and the single inference time of the model on the CPU is <45 ms. This serial WDM fiber grating demodulation method can be effectively applied in temperature and strain measurement demodulation.
In fiber Bragg grating (FBG) sensor networks, the highly overlapped spectral signals can lead to considerable errors in center wavelength demodulation. To tackle this problem, we utilize the fully convolutional time-domain audio separation network (Conv-TasNet) model to produce a distinct spectral signal, which is then demodulated using the dual-weight centroid approach to determine the spectral signal’s center wavelength. Specifically, we first demonstrate the theoretical feasibility of the Conv-TasNet model on simulated data. Experimental results show that the Conv-TasNet model can separate the signals of three FBG sensors. After that, we collect the spectral data and further train and validate the model based on the pretrained model of the simulated data to see how it performs on the real data. The experiments consistently illustrate superior performance of our Conv-TasNet model that can also separate actual spectrum signals. The same performance can be achieved by applying the pretrained model but with less training data. The model obtains a competitive performance compared to currently available methods. Moreover, the method provides a solution for improving the multiplexing performance of the FBG sensor network.
KEYWORDS: Fiber optic gyroscopes, Modulation, Interference (communication), Control systems, Gyroscopes, Tunable filters, Signal detection, Signal to noise ratio
This paper proposes a closed-loop fiber optic gyroscope control method based on the Sigma-delta modulation technique to improve the fiber optic gyroscope proportional-integral control method. Firstly, the sampling control system of digital closed-loop fiber optic gyro is established. Secondly, the fiber optic gyro digital closed-loop Sigma-delta modulator is designed and combined with the original system after the integrator. Simulation analysis is performed for both closed-loop control methods. Simulation results show that the maximum output noise amplitude of the original system is 2.8 deg/s and the maximum noise amplitude of the improved system is 0.8 deg/s under the input of a ramp signal with 100dB white noise. The noise amplitude is reduced by one order of magnitude, and the noise density is also significantly reduced. The proposed closed-loop control method of fiber optic gyro based on Sigma-delta modulation can effectively suppress the high-frequency noise of the weak signal of the gyro output, which has a promising application for the dynamic performance and noise suppression of fiber optic gyro.
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