参数与状态估计在静态MEMS惯性测量单元中的应用：教程
Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial
AI总结：
本文探讨了静态MEMS惯性测量单元的参数与状态估计方法，分析了误差与时间、传感器数量的关系，并通过实验验证了多传感器应用的潜力。
摘要：
惯性导航系统（INS）广泛应用于航空、海洋和陆地车辆等几乎所有运行环境。通过加速度计和陀螺仪的惯性测量，INS能够估计其主机车辆的位置、速度和方向。然而，由于固有传感器测量误差会传播到状态估计中，准确性会随时间下降。为减轻状态估计中的漂移，提出了参数和状态估计的不同方法，利用频域滤波或外部信息融合来补偿不良误差。另一种方法使用多个惯性传感器，这一领域具有快速增长的潜力和应用。观测现象的采样率增加会提高信号精度、频率分辨率、噪声抑制和冗余性等关键因素。本文提供了一个基本多惯性操作的分析教程，从新的视角探讨误差与时间、传感器数量的关系。为此，采用静止和水平排列的传感器阵列，并分析其对仪器误差的鲁棒性。随后，假设的分析模型与实验结果进行比较，并讨论两者之间的一致性水平。最终，我们的结果展示了使用多个传感器的巨大潜力，我们观察到从信号层面到导航状态的改进。本文教程适用于初学者和熟悉多惯性传感器的人群。
英文摘要：
Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, accuracy degrades over time. To mitigate the resulting drift in state estimates, different approaches of parametric and state estimation are proposed to compensate for undesirable errors, using frequency-domain filtering or external information fusion. Another approach uses multiple inertial sensors, a field with rapid growth potential and applications. The increased sampling of the observed phenomenon results in the improvement of several key factors such as signal accuracy, frequency resolution, noise rejection, and higher redundancy. This study offers an analysis tutorial of basic multiple inertial operation, with a new perspective on the error relationship to time, and number of sensors. To that end, a stationary and levelled sensors array is taken, and its robustness against the instrumental errors is analyzed. Subsequently, the hypothesized analytical model is compared with the experimental results, and the level of agreement between them is thoroughly discussed. Ultimately, our results showcase the vast potential of employing multiple sensors, as we observe improvements spanning from the signal level to the navigation states. This tutorial is suitable for both newcomers and people experienced with multiple inertial sensors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
