【作者】 王璐；

【导师】 褚东升；

【作者基本信息】 中国海洋大学 ， 信号与信息处理， 2004， 硕士

【摘要】 随机信号处理的重要课题之一是某种意义下的信号最优估计，即从受到噪声污染的观测信号中提取有用信息。本文主要针对多通道带乘性噪声系统的观测噪声最优估计算法和状态最优融合估计算法展开进一步研究。同时，本课题由国家自然科学基金数学天元基金(A0324676)及教育部科学技术研究重点项目(02131)资助。 目前针对多通道带乘性噪声系统的研究还不够完善。在噪声估计方面，以往的研究仅限于动态噪声估计，即系统的反褶积估计，然而估计系统的观测噪声有助于识别不同的噪声源，为有效消除噪声提供指导。同时，在石油地震勘探的噪声压制问题中也需要对观测噪声进行估计。因此，研究观测噪声的最优估计问题同样具有重要的理论价值和实际意义。本文主要针对乘性噪声为一般矩阵这种真正意义下的多通道带乘性噪声系统在各种噪声条件下对观测噪声的最优估计算法做了深入的研究探讨。此外，随着科学技术尤其是电子计算机技术的飞速发展，多传感器信息融合技术由于其高精度和高可靠性在各个领域日益得到广泛的应用。因此，本文在前人工作的基础上，还针对多传感器观测下的多通道带乘性噪声系统对其最优估计融合算法做了进一步研究探讨。 本文的主要研究工作如下： 第一，本文利用Hilbert空间理论中的正交投影定理及新息序列的性质就独立白噪声情形和动态噪声一步相关并与观测噪声在同时刻和过去相邻时刻也相关的复杂情形分别推导出了在线性最小方差意义下的多通道带乘性噪声系统的观测噪声最优滤波估计算法和最优平滑估计算法，进一步丰富了多通道带乘性噪声系统的噪声估计理论。 第二，本文针对多传感器观测下的多通道带乘性噪声系统，在乘性噪声为一般矩阵且各个传感器的观测噪声分别与系统的动态噪声同时刻相关的条件下给出了状态最优滤波融合算法、最优固定域平滑算法及最优固定域反褶积算法。根据融合策略的不同，本文还提出了集中式和分布式两种滤波融合算法，其中分布式融合算法又分为带反馈与不带反馈两种实现形式。 第三，本文进行了大量仿真研究，仿真结果表明上述算法有较好的估计效果。更多还原

【Abstract】 One of the important problem in stochastic signal processing is optimal estimation of signal under certain meanings, or obtaining useful information from signals polluted by noises. The optimal estimation algorithm of measurement noise and the optimal state fusion algorithm for multi-channel system with multiplicative noises are mainly researched in this dissertation. This work is supported by NSFS for Tianyuan Mathematical Fund Grant #A03 24676 and the Ministry of Education of China Grant #02131.At present the study of multi-channel system with multiplicative noises is not perfect. In the field of noise estimation, previous studys are only limited at dynamic noise estimation, but the measurement noise estimation has also important theory value and practical significance. This dissertation focuses on the optimal estimation of measurement noise under the condition that the multiplicative noise is in the form of a general stochastic matrix, which means a genuine multi-channel system. Furthermore, with the rapid development of computer technology there has been a considerable interest in the multi-sensor system because of its high accuracy and high reliability. Accordingly, the multi-sensor information fusion technique has received deep researches and has been used in military systems, oil seismic exploration and underwater remote targets detection, etc. Based on the former works, this dissertation also discusses the optimal fusion estimation algorithms.The main researching contents of this dissertation are as follows:1. In this dissertation an optimal filtering algorithm and an optimal smoothing algorithm of measurement noise are developed for multi-channel system with multiplicative noises under the conditions that the additive noises are independent white noises and the dynamic noise correlates itself in one-step and correlates with the measurement noise at the present step as well as one past step. These algorithms are optimal in the sense of linear minimum-variance.2. For the multi-channel system with multiplicative noises under multi-sensor observation an optimal fusion estimation algorithm is proposed on the conditions that the multiplicative noise is in the form of a general stochastic matrix and the measurement noises of each sensor are correlated with the dynamic noise. This expands the application of multi-sensor systems.3. The above algorithms are tested by many simulations, which show the validity of these algorithms.更多还原