摘要
当容积卡尔曼滤波的系统模型不准确或测量出现异常时容易出现滤波发散。为了解决这一问题,提出了一种自适应容积卡尔曼滤波算法,构造了一组噪声统计估计器对噪声的统计特征进行在线实时估计,并在测量异常时采用修正函数对滤波过程进行修正,有效提高了滤波估计的精度和对滤波发散的抑制能力;在集中式滤波结构和联邦式滤波结构的基础上,设计了一种基于自适应容积卡尔曼滤波算法的多传感器系统混合式组合滤波结构,并给出了融合各传感器的局部滤波信息以得到全局滤波估计的计算方法。以对车辆的定位导航为应用背景进行了仿真实验,仿真结果证明了所提方法的有效性。
Cubature Kalman filter is prone to filtering divergence if the system model is inaccurate or measuring abnormal In order to solve this problem, this paper proposed the adaptive cubature Kalman filter algorithm. It constructed a group of noise statistic estimators to estimate the statistical characteristic of the noise in real time. When the measurement was abnormal, it adopted correction function to adjust the filtering process, and thus the estimation accuracy and the ability to restrain the filte- ring divergence could be improved effectively. On the basis of centralized filter structure and federal filter structure, this paper designed a kinds of hybrid composite filter structure about nmlti-sensor system based on adaptive eubature Kalman filter algo- rithm, and gave the method that fused the local filtering information of each sensor＇ s to get the global filtering information. In the application background of positioning and navigation for vehicle to make simulation tests, and the results show the effec- tiveness of the proposed method.
出处
《计算机应用研究》
CSCD
北大核心
2014年第5期1312-1315,1331,共5页
Application Research of Computers
基金
江苏省自然科学基金资助项目(BK2009061)
全军军事学资助项目(2010JY0284-159)
