Zhang Shixue, Wang Houfeng, Song Liduo, Li Hongwen, Liu Shuai
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 Sep 9;24(17):5847. doi: 10.3390/s24175847.
This study presents a novel approach to address the autonomous stable tracking issue in electro-optical theodolite operating in closed-loop mode. The proposed methodology includes a multi-sensor adaptive weighted fusion algorithm and a fusion tracking algorithm based on a three-state transition model. A refined recursive formula for error covariance estimation is developed by integrating attenuation factors and least squares extrapolation. This formula is employed to formulate a multi-sensor weighted fusion algorithm that utilizes error covariance estimation. By assigning weighted coefficients to calculate the residual of the newly introduced error term and defining the sensor's unique states based on these coefficients, a fusion tracking algorithm grounded on the three-state transition model is introduced. In cases of interference or sensor failure, the algorithm either computes the weighted fusion value of the multi-sensor measurement or triggers autonomous sensor switching to ensure the autonomous and stable measurement of the theodolite. Experimental results indicate that when a specific sensor is affected by interference or the off-target amount cannot be extracted, the algorithm can swiftly switch to an alternative sensor. This capability facilitates the precise and consistent generation of data, thereby ensuring the stable operation of the tracking system. Furthermore, the algorithm demonstrates robustness across various measurement scenarios.
本研究提出了一种新颖的方法来解决闭环模式下光电经纬仪的自主稳定跟踪问题。所提出的方法包括一种多传感器自适应加权融合算法和一种基于三态转换模型的融合跟踪算法。通过整合衰减因子和最小二乘外推法，开发了一种用于误差协方差估计的改进递归公式。该公式用于制定一种利用误差协方差估计的多传感器加权融合算法。通过分配加权系数来计算新引入误差项的残差，并基于这些系数定义传感器的独特状态，引入了一种基于三态转换模型的融合跟踪算法。在出现干扰或传感器故障的情况下，该算法要么计算多传感器测量的加权融合值，要么触发自主传感器切换，以确保经纬仪的自主稳定测量。实验结果表明，当特定传感器受到干扰或无法提取脱靶量时，该算法可以迅速切换到替代传感器。这种能力有助于精确一致地生成数据，从而确保跟踪系统的稳定运行。此外，该算法在各种测量场景中都表现出鲁棒性。
