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作 者:朱枫[1,2,3] 张葆[1,2] 李贤涛 王正玺[1,2,3] 张士涛
机构地区:[1]中国科学院长春光学精密机械与物理研究所,长春130033 [2]中国科学院航空光学成像与测量重点实验室,长春130033 [3]中国科学院大学,北京100049
出 处:《吉林大学学报(工学版)》2017年第6期1868-1875,共8页Journal of Jilin University:Engineering and Technology Edition
基 金:"863"国家高技术研究发展计划重点项目(2013AA122102)
摘 要:针对陀螺传感器测量精度低,严重影响航空光电稳定平台视轴稳定精度的问题,设计了强跟踪卡尔曼滤波器。根据时间序列分析法的基本原理,对经过预处理的陀螺原始量测数据进行AR模型建模,根据该AR模型采用状态空间法设计卡尔曼滤波器,同时为了增强系统的鲁棒性,引入强跟踪算法对卡尔曼滤波中的状态预测方差进行实时调整,构造了强跟踪卡尔曼滤波器。阐述了该算法的理论、原理,并且在某型航空光电稳定平台上进行试验验证。试验结果表明:强跟踪卡尔曼滤波器输出信号的方差减少了44.1%,分散程度降低,同时,相较于巴特沃斯滤波器,最大超调量减少13%,上升时间缩短了3ms,调整时间缩短了37.5ms,使平台具有良好的动态性能。研究表明,强跟踪卡尔曼滤波器可以提高航空光电稳定平台的精度,有较高的实用价值。To overcome the problem that the low measuring precision of the gyroscope leads to degradation of Line of Sight (LOS) stabilization accuracy of photoelectric stabilized platform for aviation, a strong tracking Kalman filter was designed. According to the principle of the time sequence analysis from the random series, Autoregressive method (AR) was adopted to model the preprocessed data measured by the gyroscope. The state-space method was used to design the Kalman filter. In the meantime, in order to increase the control system's robustness, the strong tracking algorithm was introduced to correct the variance of state prediction in real time to construct the strong tracking Kalman filter. The theory and principle of the algorithm were described and applied to the photoelectric stabilized platform for aviation. The results show that the variance of signal was reduced by 44.1% after filtering using the strong tracking Kalman filter, and the dispersion was apparently reduced. At the same time, comparing with the Butterworth filter, the strong tracking Kalman filler shortens the overshoot by 13%. cut clown the rise time by 3 ms. shortens the regulation time by 37.3 ms. keeps tile platform in good dynamic properties. The study indicated that the strong tracking Kalman filler has strong application value.
关 键 词:自动控制技术 视轴稳定精度 强跟踪卡尔曼滤波 陀螺传感器
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置]
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