| 引用本文: | 尹大伟,廖瑛,王雷,等.基于抗差Kalman滤波的航空发动机测试数据预处理技术.[J].国防科技大学学报,2010,32(4):55-60.[点击复制] |
| YIN Dawei,LIAO Ying,WANG Lei,et al.Technology of Aeroengine Testing Data Preprocessing Based on Robust Kalman Filter[J].Journal of National University of Defense Technology,2010,32(4):55-60[点击复制] |
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| 基于抗差Kalman滤波的航空发动机测试数据预处理技术 |
| 尹大伟1, 廖瑛1, 王雷2, 梁加红2 |
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(1.国防科技大学 航天与材料工程学院，湖南 长沙 410073;2.国防科技大学 机电工程与自动化学院，湖南 长沙 410073)
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| 摘要: |
| 针对使用标准Kalman滤波算法不能准确处理包含粗差的航空发动机测试数据的问题，在分析标准Kalman滤波算法准则和观测误差对滤波估计结果影响的基础上，采用动态调整观测信息在滤波估计结果中权重的方法，给出了基于抗差M估计理论的抗差Kalman滤波准则和递推公式。对不同的发动机测试数据分别采取序列滤波的方法，减少了运算量。基于常加速度模型，建立了测量参数的状态空间方程和测量方程。以包含粗差的某型涡扇发动机稳定工作过程的模拟测量数据为例，采用所设计的抗差Kalman滤波器对其进行预处理，与标准Kalman滤波算法处理的结果对比表明，在模型误差一定的情况下，抗差Kalman滤波算法具有更好的估计精度。 |
| 关键词: 航空发动机 抗差Kalman滤波 预处理 M估计 最小二乘 |
| DOI： |
| 投稿日期：2010-05-27 |
| 基金项目: |
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| Technology of Aeroengine Testing Data Preprocessing Based on Robust Kalman Filter |
| YIN Dawei1, LIAO Ying1, WANG Lei2, LIANG jiahong2 |
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(1.College of Aerospace and Materials Engineering, National Univ. of Defense Technology， Changsha 410073, China;2.College of Mechatronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| The standard Kalman filter algorithm cannot accurately preprocess the measured data of aeroengine with exceptional errors. The principle of standard Kalman filter and the impact of test errors to the filter estimate results were analysed, and the method of dynamically adjusting the weight of observation information in the filter estimate result was introduced. Then, based on M-estimation theory, the Robust Kalman filter principle and the recursion formula were presented. The state-space equations and observation equations of the measured parameters were established in terms of CA(Constant Acceleration)model. In order to decrease the calculation consumption, the sequence filter was applied separately to process the different sensed data. Furthermore, the preprocessing to the simulation sensed data of a given turbofan engine's steady operation was carried out as an example, using the given Robust Kalman filter. The calculation results, compared with standard Kalman filter, show that the designed Robust Kalman filter has better estimate precision with a given model error. |
| Keywords: aeroengine Robust Kalman filter preprocess M-estimation least squares |
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