Brunson Benjamin, Wang Jianguo, Ma Wenbo
Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.
Sensors (Basel). 2024 Dec 4;24(23):7754. doi: 10.3390/s24237754.
This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors' previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors. This research explores the feasibility of applying Variance Component Estimation (VCE) to IMU array data, using separate variance components to characterize the performance of each IMU's gyroscopes and accelerometers. This analysis is only made possible by directly modeling IMU inertial measurements under the GMIS. A real land-vehicle kinematic dataset was used to demonstrate the proposed technique. The a posteriori positioning/attitude standard deviations were compared between multi-IMU and single IMU solutions, with the multi-IMU solution providing an average accuracy improvement of ca. 14-16% in the estimated position, 30% in the estimated roll and pitch, and 40% in the estimated heading. The results of this research demonstrate that IMUs in an array do not generally exhibit homogeneous behavior, even when using the same model of tactical-grade MEMS IMU. Furthermore, VCE was used to compare the performance of three IMU sensors, which is not possible under other IMU array data fusion techniques. This research lays the groundwork for the future evaluation of IMU array sensor configurations.
本研究提出了一种将惯性测量单元（IMU）阵列集成到多传感器运动定位/导航系统中的新型建模方法。该方法利用新颖的通用多传感器集成策略（GMIS）以及作者先前研究中开发的离散卡尔曼滤波综合误差分析框架，对IMU阵列中每个IMU的传感器误差（偏差/比例因子误差）进行表征。这项工作能够对IMU阵列中所有单个传感器误差进行时变估计，以及对所有组成传感器的异常测量进行严格的故障检测和排除。本研究探讨了将方差分量估计（VCE）应用于IMU阵列数据的可行性，使用单独的方差分量来表征每个IMU的陀螺仪和加速度计的性能。只有通过在GMIS下直接对IMU惯性测量进行建模，才能进行这种分析。使用了一个真实的陆地车辆运动数据集来演示所提出的技术。比较了多IMU和单IMU解决方案的后验定位/姿态标准偏差，多IMU解决方案在估计位置上平均精度提高了约14 - 16%，在估计横滚和俯仰上提高了30%，在估计航向角上提高了40%。本研究结果表明，即使使用相同型号的战术级MEMS IMU，阵列中的IMU通常也不会表现出均匀的行为。此外，VCE被用于比较三个IMU传感器的性能，这在其他IMU阵列数据融合技术下是不可能的。本研究为未来评估IMU阵列传感器配置奠定了基础。
