【作者】 高媛；

【导师】 邓自立；

【作者基本信息】 黑龙江大学 ， 微电子学与固体电子学， 2010， 博士

【摘要】 随着计算机技术、通信技术、微电子技术、精密机械制造技术以及控制技术的飞速发展,各种面向复杂应用背景的多传感器系统大量涌现。在多传感器系统中,由于信息表现出形式多样、数量巨大、关系复杂以及要求处理及时、准确和可靠等特征,为了提高信息的综合处理能力,一门由信息学科、控制学科等交叉、综合、拓展而产生的新兴学科——多传感器信息融合应运而生。多传感器信息融合估计是多传感器信息融合的一个重要分支,其目的是如何利用多传感器提供的观测数据对系统状态或信号给出比基于单传感器更精确的估计,广泛应用于目标跟踪、军事、航天、制导、GPS定位、机器人等高技术领域,具有重要理论和应用意义。本课题来源于国家自然科学基金项目“多传感器信息融合最优和自校正滤波新理论和新方法”(60374026)和“自校正信息融合滤波理论及其应用研究”(60874063)。本文基于现代时间序列分析方法,以理论分析为主,以计算机仿真示例为辅,对多传感器线性随机系统的最优加权观测融合状态估计和自校正加权观测融合状态估计问题进行了深入研究。做了如下几方面工作：首先,对于带未知噪声统计的多传感器系统,利用相关方法,提出了一种具有强一致性的信息融合噪声统计在线估值器,其中从采样相关函数线性方程组中任意选择非奇异的部分线性方程组求解得局部噪声统计估值器,然后取局部估值器的算术加权平均作为融合估值器,证明了它可被视为最小二乘融合器。用相关函数的遍历性证明了它以概率1收敛于真实的噪声统计。它的精度介于局部估值器中最高和最低的精度之间,因而具有较高的可信度。对于含有未知模型参数和噪声统计的多传感器ARMA(自回归滑动平均)系统,利用已有的ARMA模型参数估计方法,结合未知噪声统计的信息融合在线估值器,提出了多段信息融合辨识方法,并且提出了一种信息融合模型参数估值器概念,它是基于各传感器得到的局部模型参数估值器的算术平均,且从理论上严格证明了所提出的信息融合模型参数估值器的一致性。其次,对带相关观测噪声和带相关噪声的多传感器线性离散定常随机系统,在线性无偏最小方差(LUMV)融合准则下,应用Lagrange乘数法和基于ARMA新息模型的滤波理论,分别提出了最优加权观测融合稳态Kalman滤波和Wiener滤波算法,其结果等同于基于加权最小二乘(WLS)融合准则的最优加权观测融合滤波算法,兼具完全功能等价性和渐近全局最优性。对于带不同观测阵、相关观测噪声的多传感器系统,应用矩阵分块方法,结合观测加权阵的约束条件,提出了降维观测融合算法,可以显著地减少求观测加权阵和融合观测误差方差阵的计算负担,给出的计算负担比较表从定量分析的角度支持了此论断。第三,对于带有未知噪声统计、相关观测噪声和相关噪声的多传感器系统,将未知噪声统计的信息融合在线估值器代入稳态最优加权观测融合滤波器,分别提出了自校正加权观测融合Kalman滤波器和Wiener滤波器,及自校正降维观测融合滤波器。利用动态误差系统分析(DESA)方法证明了自校正融合器按实现收敛于相应的稳态全局最优融合器,即具有渐近全局最优性。最后,对于带公共干扰噪声和含有未知噪声统计和未知模型参数的多传感器单通道自回归(AR)信号系统和ARMA信号系统,利用ARMA模型到状态空间模型的转化方法,将信号估计问题转化为状态估计问题。将模型参数和噪声统计的信息融合估值器代入稳态最优加权观测融合Wiener信号滤波器中,分别提出了自校正加权观测融合Wiener信号滤波器。应用动态误差系统分析(DESA)方法,严格证明了提出的自校正加权观测融合Wiener信号滤波器按实现收敛于相应的稳态全局最优滤波器,因而具有渐近全局最优性。通篇用大量的在跟踪系统中和在信号处理中的仿真例子说明了所提出方法和算法的有效性。更多还原

【Abstract】 With the rapid development in computer technology, communication technology, microelectonics technology, precision machine manufacture technology and control technology, all kinds of multisensor systems, suited to the complicated application backgrounds, come into being. In the multisensor systems, since the information has a lot of characters, for example, it has the various forms, the tremendous quantities, the complicated relationship, and it requires to be precise and reliable, and settled in time, in order to increase the synthesized handling ability, a new branch of science, multisensor information fusion, intersected, synthesized and expended by information subject and control subject, comes into being.Multisensor information fusion estimation is an important branch of multisensor information fusion, and its aim is how to utilize the measurement data, provided by multisensor, to make more precise estimation to the state or signal of the systems than the estimation based on the single sensor. It is widely used in many high technology fields, such as target tracking, military affairs, space flight, guidance, GPS position and robotics.This subject comes from the National Nature Science Foundation under Grant 60374026, New Theories and New Methods for Multisensor Information Fusion Optimal and Self-tuning Filtering, and Grant 60874063, Research on Self-tuning Information Fusion Filtering Theory and Its Applications.Based on modern time series analysis method, for multisensor linear stochastic systems, this paper makes the theoretical analysis primarily, the computation simulation examples secondarily, and the deep researches on the state estimation problems of optimal weighted measurement fusion and self-tuning weighted measurement fusion. The main works are as follows:First, for the multisensor systems with unknown noise statistics, applying the correlation method, the information fusion noise statistics on-line estimators, which have the strong consistence, are presented, where the nonsingular partial linear equations are selected from the sampled correlation function linear equations to be solved to obtain the local noise statistics estimators, then the fused estimators are obtained by making the weighted arithmetic average of the local estimators, and it is proved that the fused estimators can be viewed as the least squares fusers. Using the ergodicity of the correlation function, it is proved that the fused estimators converge to the true noise statistics with probability one. Since the accuracy of each fused estimator falls in between the highest and lowest accuracies of the local estimators, it has the higher reliability. For the multisensor autoregressive moving average (ARMA) systems with unknown model parameters and noise statistics, applying the existing estimation methods for ARMA model parameters, combining the information fusion on-line estimators of the unknown noise statistics, the multi-stage information fusion identification methods and the concept of the information fusion model parameter estimators are presented. The information fusion model parameter estimators are obtained by taking the average of the local model parameter estimators obtained from each sensor. In theory, the consistence of the proposed information fusion model parameter estimators is rigorously proved.Second, for the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises and correlated noises, under the linear unbiased minimum variance (LUMV) criterion, applying Lagrange multiplier method and the filtering theories based on ARMA innovation model, the optimal weighted measurement fusion steady-state Kalman filtering and Wiener filtering algorithms are presented respectively, and theirs results are equal to those of the optimal weighted measurement fusion filtering algorithms based on the weighted least squares (WLS) fusion criterion, that is, they have the completely functional equivalence and the asymptotical global optimality. For the multisensor systems with different measurement matrices and correlated measurement noises, using the matrix partitioning method, combining the constraint condition of the measurement weight matrices, the reduced dimension measurement fusion algorithm is presented, which can remarkably reduce the computational burdens of computing the measurement weight matrices and the fused measurement error variance matrices. The presented comparison table of the computational burden supports this inference from the view point of quantitative analysis.Third, for the multisensor systems with unknown noise statistics, correlated measurement noises and correlated noises, by substituting the information fusion on-line estimators of unknown noise statistics into the steady-state optimal weighted measurement fusion filters, the self-tuning weighted measurement fusion Kalman filters and Wiener filters, and the self-tuning reduced dimension measurement fusion filters are presented respectively. Applying the dynamic error system analysis (DESA) method, it is proved that the proposed self-tuning fusers converge to the corresponding steady-state globally optimal fusers in a realization, that is, they have the asymptotical global optimality.Last, for the multisensor single channel autoregressive (AR) signal systems and ARMA signal systems with the common disturbance noise, unknown noise statistics and unknown model parameters, using the transformation method from ARMA model to the state space model, the signal estimation problem is transformed into the state estimation problem. By substituting the information fusion estimators of model parameters and noise statistics into the steady-state optimal weighted measurement fusion Wiener signal filters, the self-tuning weighted measurement fusion Wiener signal filters are presented respectively. By DESA method, it is rigorously proved that the proposed self-tuning weighted measurement fusion Wiener signal filters converge to the corresponding steady-state globally optimal filters in a realization, so they have the asymptotical global optimality. In the whole paper, a lot of simulation examples in the tracking system and the signal processing show the effectiveness of the proposed methods and algorithms.更多还原