自校正信息融合状态与信号Wiener估值器

【作者】 刘金芳；

【导师】 邓自立；

【作者基本信息】 黑龙江大学 ， 控制理论与控制工程， 2007， 硕士

【摘要】 近年来,随着计算机技术、通讯技术的发展,多传感器信息融合技术得到了迅速发展,并成为当前信息领域一个十分活跃的研究热点。多传感器信息融合又称为多传感器数据融合,它使用多个传感器对同一目标进行检测,避免了单个传感器的局限性,从而提供更加全面准确的信息。多传感器信息融合作为多源信息综合处理的一项新技术,能够合成来自某一目标的多源信息,产生比单一信息源更精确的估计。对于含有未知模型参数和噪声(有色噪声及白噪声)统计的多传感器单输出系统,应用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型的递推增广最小二乘法或两段递推增广最小二乘法可在线估计未知模型参数和噪声统计。在按状态分量标量加权线性最小方差最优信息融合准则下,分别对含有未知有色观测噪声或未知有色输入噪声的单输出系统提出了自校正分量解耦信息融合Wiener状态估值器;在按标量加权线性最小方差最优信息融合准则下,分别对含有未知模型参数或未知有色观测噪声的单输出系统提出了ARMA信号自校正信息融合Wiener估值器。证明了它们的收敛性,即若ARMA新息模型的参数估计是一致的,则自校正信息融合Wiener估值器将收敛于当模型参数和噪声方差己知时的最优信息融合Wiener估值器,且自校正信息融合Wiener估值器的精度高于每个局部自校正Wiener估值器。大量跟踪系统的仿真例子说明了其正确性和有效性。更多还原

【Abstract】 Recently, with the development of computer and communication technology, multisensor information fusion technology develops rapidly and becomes an active research focus in current information domain. Multisensor information fusion is also called multisensor data fusion, which uses multisensor to detect the same object, avoids the limitation of single sensor, so it can offer more general and accurate information. As a new technology for multisource date processing, multisensor information fusion can synthesize multisource date from one object, generate more accurate estimation than single source.For multisensor single output systems with unknown model parameters and noise (colored and white noise) statistics, using the model time series analysis method, based on recursive extend least squares method or two-stage recursive extend least squares method of the autoregressive average moving innovation model, the unknown model parameters and noise statistics can be obtained on line. Under the linear minimum variance optimal information fusion criterion weighted by scales for state components, self-tuning decoupled fusion Wiener state estimators for the single output system with unknown colored measurement (or input) noise are presented respectively; under the linear minimum variance optimal information fusion criterion weighted by scales, ARMA signal self-tuning fusion Wiener estimators for the single output system with unknown model parameters or measurement noise are presented respectively. The convergence of them is proved, i.e if the parameters estimation is consistent, then self-tuning fusion estimator will converge to the optimal fusion estimator with known model parameters and noise statistics, and the accuracy of self-tuning fusion estimator is higher than that of each local self-tuning estimator. Many simulation examples of tracking system show their effectiveness.更多还原