Li Hongcai, Liang Zhe, Zhou Zhaofa, Zhang Zhili, Zhao Junyang, Tian Longjie
Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi'an 710025, China.
Institute of Optics and Electronics, School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China.
Micromachines (Basel). 2025 Jul 29;16(8):884. doi: 10.3390/mi16080884.
The random error of fiber optic gyros is a critical factor affecting their measurement accuracy. However, the statistical characteristics of these errors exhibit time-varying properties, which degrade model fidelity and consequently impair the performance of random error suppression algorithms. To address these issues, this study first proposes a recursive dynamic Allan variance calculation method that effectively mitigates the poor real-time performance and spectral leakage inherent in conventional dynamic Allan variance techniques. Subsequently, the recursive dynamic Allan variance is integrated with the process variance estimation of Kalman filtering to construct a dual-adaptive Kalman filter capable of autonomously switching and adjusting between model parameters and noise variance. Finally, both static and dynamic validation experiments were conducted to evaluate the proposed method. The experimental results demonstrate that, compared to existing algorithms, the proposed approach significantly enhances the suppression of angular random walk errors in fiber optic gyros.
光纤陀螺的随机误差是影响其测量精度的关键因素。然而，这些误差的统计特性呈现出时变特性，这会降低模型保真度，进而损害随机误差抑制算法的性能。为了解决这些问题，本研究首先提出了一种递归动态阿伦方差计算方法，该方法有效缓解了传统动态阿伦方差技术固有的实时性差和频谱泄漏问题。随后，将递归动态阿伦方差与卡尔曼滤波的过程方差估计相结合，构建了一种能够在模型参数和噪声方差之间自主切换和调整的双自适应卡尔曼滤波器。最后，进行了静态和动态验证实验来评估所提方法。实验结果表明，与现有算法相比，所提方法显著增强了对光纤陀螺角度随机游走误差的抑制能力。
