摘要
将模型的动态系统分析与具有统计特性的多尺度信号变换方法相结合,首先将状态方程采用数据块变换的方式以得到新的状态块方程,并将量测方程表达为数据块的形式;然后将量测向量进行多层小波变换以得到新的量测向量,并结合状态块方程进行卡尔曼滤波;最后根据卡尔曼滤波结果建立多尺度分布式融合估计算法。仿真结果表明,相对于原始尺度的集中式卡尔曼滤波器及原始尺度的多尺度融合算法,本算法可明显地提高系统的滤波精度。
This paper combines the model＇s dynamic analysis method with multi-scale transformation method of statistical characteristics. At first data-block transformation technique is used to change the original state equation into the new state-block equation, and the originalmeasurement equation is expressed in the form of data-block. Then, the multi-layer wavelet transformation technique is used to the original measurement vector to achieve the new measurement vector which goes into the Kalman filter with the state-block equation. At last, this paper builds up the multi-scale distributed fusion estimation algorithm according to the above results of Kalman filter. When the above algorithm is used to GPS/SST/SIN integrated navigation system, the simulated results show that this algorithm can increase the filtering precision.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2012年第7期823-826,共4页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(60874112)
"泰山学者"建设工程专项经费资助项目
关键词
多尺度
分布式融合
多传感器组合导航
卡尔曼滤波
融合算法
multi-scale
distributed fusion
multi-sensor integrated navigation system
Kalman filter
fusion algorithm
