Chen Xiyuan, Shen Chong
School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Appl Opt. 2012 Jun 10;51(17):3755-62. doi: 10.1364/AO.51.003755.
A novel adaptive forward linear prediction (FLP) denoising algorithm and a temperature drift modeling and compensation concept based on ambient temperature change rate for fiber-optic gyroscope (FOG) are presented to calibrate the errors caused by intense ambient temperature variation. The intense ambient temperature variation will bring large temperature errors, which will degrade the performance of FOG. To analyze the temperature variation, characteristics of FOG temperature experiments are developed at first. Then the adaptive FLP denoising algorithm is employed to eliminate the noise aiming at reducing noise interference. After that, a simple modeling concept of building the compensation model between temperature drift and ambient temperature change rate is first to be given (we have not found a report of better results in any literature). The semiphysical simulation results show that the proposed method significantly reduces the noise and drift caused by intense ambient temperature variation.
提出了一种新型自适应前向线性预测（FLP）去噪算法以及一种基于环境温度变化率的光纤陀螺仪（FOG）温度漂移建模与补偿概念，以校准由强烈环境温度变化引起的误差。强烈的环境温度变化会带来较大的温度误差，这将降低光纤陀螺仪的性能。为了分析温度变化，首先开展了光纤陀螺仪温度实验的特性研究。然后采用自适应FLP去噪算法来消除噪声，旨在减少噪声干扰。在此之后，首次给出了一种简单的建模概念，即建立温度漂移与环境温度变化率之间的补偿模型（我们在任何文献中都未发现有更好结果的报道）。半物理仿真结果表明，所提出的方法显著降低了由强烈环境温度变化引起的噪声和漂移。
