A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.
A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.
复制标题
DOI：
10.3390/s130809549
复制
发表时间：
2013-07-24
期刊：
影响因子：
--
通讯作者：
Ferrer C
中科院分区：
文献类型：
--
作者：
Quinchia AG;Falco G;Falletti E;Dovis F;Ferrer C
微电子机械系统(MEMS)的发展使得制造廉价的小尺寸加速度计和陀螺仪成为可能，这些加速度计和陀螺仪被用于全球定位系统(GPS)和惯性导航系统(INS)集成的许多应用中，如识别轨迹缺陷、地面和行人导航、无人机(UAV)、许多平台的稳定等。虽然这些MEMS传感器成本低，但它们存在不同的误差，在短时间内降低了导航系统的精度。因此，有必要对这些误差进行适当的建模，以使它们最小化，从而提高系统性能。在这项工作中，显示和比较了目前最常用的分析影响这些传感器的随机误差的技术：我们详细地研究了自相关、Allan方差(AV)和功率谱密度(PSD)技术。随后，对惯性传感器进行了自回归(AR)滤波和小波去噪相结合的分析和建模。由于低成本惯导系统(MEMS级)提供的误差源具有短期(高频)和长期(低频)分量，我们介绍了一种通过对Allan方差进行完整分析、小波去噪和选择分解级别来补偿这些误差项的方法，以便在这些技术之间进行适当的组合。最后，为了对利用这些技术得到的随机模型进行评估，对松散耦合GPS/INS组合策略的扩展卡尔曼滤波(EKF)进行了不同状态的扩充。结果表明，在GPS信号阻塞的情况下，该方法与传统的传感器误差模型在城市道路上采集的实际数据进行了比较。
Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.
登录
查看更多内容
DOI：
10.3390/s120202219
发表时间：
2012
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Hidalgo J;Poulakis P;Köhler J;Del-Cerro J;Barrientos A
通讯作者：
Barrientos A
影响因子：
3.6
作者：
El-Sheimy, N;Nassar, S;Noureldin, A
通讯作者：
Noureldin, A
DOI：
10.1109/tim.2007.908635
发表时间：
2008-01-01
影响因子：
5.6
作者：
EI-Sheimy, Naser;Hou, Haiying;Niu, Xiaoji
通讯作者：
Niu, Xiaoji
影响因子：
2.4
作者：
El-Diasty, Mohammed;Pagiatakis, Spiros
通讯作者：
Pagiatakis, Spiros
DOI：
10.3390/s91108473
发表时间：
2009
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
El-Diasty M;Pagiatakis S
通讯作者：
Pagiatakis S