| 引用本文: | 王鼎杰,吕汉峰,吴杰.基于微惯导随机误差时间序列建模的改进组合导航方法.[J].国防科技大学学报,2016,38(6):64-69.[点击复制] |
| WANG Dingjie,LYU Hanfeng,WU Jie.ARMA-based stochastic modeling method for improving the performance of low-cost MIMU/GNSS integration[J].Journal of National University of Defense Technology,2016,38(6):64-69[点击复制] |
|
| |
|
|
| 本文已被：浏览 7739次 下载 6684次 |
| 基于微惯导随机误差时间序列建模的改进组合导航方法 |
| 王鼎杰, 吕汉峰, 吴杰 |
|
(国防科技大学 航天科学与工程学院， 湖南 长沙 410073)
|
| 摘要: |
| 针对低精度、低成本微机电惯性测量单元随机误差建模效果不理想会极大影响组合导航性能的难题，采用时间序列分析方法建立了微机电惯性测量单元随机误差的自回归滑动平均模型，通过对卡尔曼滤波器的状态变量进行增广，建立系统动力学方程和观测方程，实现对零偏误差的在线估计。实测数据分析验证了该随机误差建模的有效性。实测数据处理结果表明，该方法能够显著提高低成本微惯性解算外推精度，增强微惯性/卫星组合导航可靠性。 |
| 关键词: 微机电系统 惯性测量单元 随机建模 自回归滑动平均 扩展卡尔曼滤波 |
| DOI：10.11887/j.cn.201606011 |
| 投稿日期：2015-07-11 |
| 基金项目:国防科技大学研究生创新资助项目（B140103） |
|
| ARMA-based stochastic modeling method for improving the performance of low-cost MIMU/GNSS integration |
| WANG Dingjie, LYU Hanfeng, WU Jie |
|
(College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China)
|
| Abstract: |
| High noise and complicated errors caused by low-cost MIMU (micro-electro-mechanical system-based inertial measurement unit, MEMS-based IMU) have caused its stochastic modeling challenge, which may undermine the performance of inertial-based integrated navigation. In order to achieve accurate MEMS-based navigation, a stochastic modeling method called auto-regressive moving-average model for low-cost MEMS-based inertial sensors was proposed on the basis of time series analysis theory. This model was then expanded into the state variables of the conventional Kalman filter to establish the system dynamic equation and observation equation and to estimate the zero-bias online. Field test results indicate that the proposed algorithm can not only realize a highly accurate autonomous navigation for low cost MIMU, but also provide reliability to the MIMU/GNSS integrated system. |
| Keywords: micro-electro-mechanical system inertial measurement unit stochastic modeling auto-regressive moving-average extended Kalman filtering |
|
|
