微机械系统（Microelectro mechanical systems, MEMS）传感器作为空天信息采集的最重要工具之一，对其时/空耦合误差的实时快速检测与校正是保障传感器信息安全和数据处理可靠的关键。现有传感器误差检测与校正算法中，基于传统卡尔曼滤波（Kalman filtering, KF）的方法实时性好、计算成本低，但在非线性、非高斯噪声及强信号干扰下鲁棒性不足；基于深度学习的方法虽对误差检测与校正效果较好，但存在计算成本高、实时性差等问题。为解决这些问题，本文将上述两类方法进行创新结合，提出一种基于双重动态饱和机制的空天MEMS传感器时/空耦合误差智能检测与校正算法。首先，借助KF 滤波思想，设计了新息饱和的误差检测算法，完成对误差的动态检测；其次，引入遗传算法对确定动态饱和阈值的核心公式中的非线性参数进行自适应寻优，扩展算法在不同数据集上的泛化性能；接着，针对遗传算法寻优效率低、迭代次数多的问题，提出一种全新的双重动态饱和机制，以降低非线性参数间的时/空耦合关系，从而提高参数寻优效率，加快收敛。最后，搭建实验平台采集MEMS传感器数据，基于实测数据的算法验证表明：相较于传统的KF 检测框架和神经网络检测框架，本文提出的误差智能检测与校正算法，在检测校正效率与精度等指标上均有明显的性能提升。
As one of the most important tools for space and aerial information collection, microelectromechanical systems (MEMS) sensors require real-time and rapid detection and correction of their time/space coupling errors to ensure the security of sensor information and the reliability of data processing. Among the existing sensor error detection and correction algorithms, the methods based on traditional Kalman filtering (KF) have good real-time performance and low computational cost, but lack robustness in the presence of nonlinearity, non-Gaussian noise, and strong signal interference. Although the methods based on deep learning have better error detection and correction effects, they suffer from high computational cost and poor real-time performance. To address these issues, this paper innovatively combines the above two types of methods and proposes an intelligent detection and correction algorithm for time/space coupling errors of space and aerial MEMS sensors based on a dual dynamic saturation mechanism. Firstly, by leveraging the idea of KF filtering, an innovation saturation-based error detection algorithm is designed to dynamically detect errors. Secondly, a genetic algorithm is introduced to adaptively optimize the nonlinear parameters in the core formula for determining the dynamic saturation threshold, thereby enhancing the algorithm's generalization performance across different datasets. Then, to address the low optimization efficiency and high number of iterations of the genetic algorithm, a novel dual dynamic saturation mechanism is proposed to reduce the time/space coupling relationship between nonlinear parameters, thereby improving the parameter optimization efficiency and accelerating convergence. Finally, an experimental platform is built to collect MEMS sensor data. The algorithm verification based on the measured data shows that, compared with the traditional KF detection framework and neural network detection framework, the proposed intelligent error detection and correction algorithm has significant performance improvements in terms of detection and correction efficiency and accuracy.
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