| 引用本文: | 党宏涛,伊国兴,于湘涛,杜祖良,王常虹.一种RVM的INS参数长期稳定性预测与补偿方法[J].哈尔滨工业大学学报,2014,46(9):15.DOI:10.11918/j.issn.0367-6234.2014.09.003 |
| DANG Hongtao,YI Guoxing,YU Xiangtao,DU Zuliang,WANG Changhong.RVM-based prediction and compensation method for the long-term stability of INS system parameters[J].Journal of Harbin Institute of Technology,2014,46(9):15.DOI:10.11918/j.issn.0367-6234.2014.09.003 |
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| 一种RVM的INS参数长期稳定性预测与补偿方法 |
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党宏涛1,3, 伊国兴1, 于湘涛2, 杜祖良1,2, 王常虹1
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(1. 哈尔滨工业大学 空间控制与惯性技术研究中心,150001哈尔滨; 2. 北京自动化控制设备研究所,100074 北京; 3. 解放军96117部队,271100 山东 莱芜)
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| 摘要: |
| 为提高惯导系统参数长期稳定性,降低人工标定成本,增强惯导系统使用效能,提出一种基于相关向量机的惯导系统参数长期稳定性预测和补偿方法,选择均值和标准差作为参数稳定性的性能指标.而对于均值随时间变化具有明显规律的参数,采用RVM方法对存贮时间较长的参数稳定性均值进行回归建模,根据模型对存贮时间较短的参数稳定性进行性能预测和标定参数补偿．最后对惯导系统中重要参数加速度计标度因数长期稳定性进行建模预测和参数补偿,补偿后结果显示,间隔时间约6个月的参数稳定性均值性能提高了50.90％,验证了所提方法具有很好的实际应用价值,且表明使用该方法能够代替人工标定,以增强惯导系统使用效能． |
| 关键词: 相关向量机 惯导系统 标定周期 长期稳定性 预测补偿 |
| DOI：10.11918/j.issn.0367-6234.2014.09.003 |
| 分类号:V241.6 |
| 基金项目:国家重点基础研究发展规划资助项目(61388010404). |
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| RVM-based prediction and compensation method for the long-term stability of INS system parameters |
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DANG Hongtao1,3, YI Guoxing1, YU Xiangtao2, DU Zuliang1,2, WANG Changhong1
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(1. Space Control and Inertial Technology Research Center, Harbin Institute of Technology, 150001 Harbin, China; 2. Beijing Institute of Automatic Control Equipment, 100074 Beijing, China; 3. 96117 Troops, 271100 Shandong Laiwu, China)
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| Abstract: |
| To improve the long-term stability performance, reduce the manual calibration costs and enhance the use efficiency of inertial navigation systems, we propose a prediction and compensation method for the long-term stability of inertial navigation system parameter based on correlation vector machine, in which we choose the mean and standard deviation as the performance indicators. For the mean parameter with a significant change with time in the law, we establish regression modeling for longer storage stability parameter by RVM method, and carry out the performance prediction and calibration parameters compensation for the parameters stability of the shorter storage. The paper presents the modeling forecasting and parameter compensation for the long-term stability of the accelerometer scale factor of the important parameters in the inertial navigation system, the parameter stability mean performance for an interval time of 6 months has improved by 50.90%. This result implies that this method can replace the manual calibration and the use efficiency of inertial navigation systems, and verifies the effectiveness of the proposed method. |
| Key words: relevance vector machine inertial navigation system calibration cycle long-term stability predictive compensation |
