随机加权自适应滤波及其在组合导航中的应用  

Random Weighting Adaptive Filtering and Its Application in Integrated Navigation

作　　者：阎海峰[1] 魏文辉[1] 赵雪华 高朝晖[1] 高社生[1] YAN Haifeng;WEI Wenhui;ZHAO Xuehua;GAO Zhaohui;GAO Shesheng(School of of Automation, Northwestern Polytechnical University, Xi＇ an 710072, China)

机构地区：[1]西北工业大学自动化学院,西安710072

出　　处：《弹箭与制导学报》2017年第5期1-5,10,共6页Journal of Projectiles,Rockets,Missiles and Guidance

基　　金：国家自然科学基金(61174193)资助

摘　　要：为了克服自适应Kalman滤波的缺点,文中提出了一种噪声有限记忆在线随机加权自适应滤波算法。在该算法中,利用随机加权和移动开窗方法分别对系统量测噪声和状态噪声的统计特性进行在线随机加权估计,并应用随机加权自适应因子进行调节,控制量测噪声和状态噪声对滤波解算精度的影响。仿真结果表明,在系统噪声统计未知情况下,所提出的噪声有限记忆在线随机加权自适应滤波算法精度明显优于传统Sage自适应Kalman滤波算法。In order to overcome the shortcomings of the adaptive Kalman filtering, an adaptive filtering algorithm based on adaptive noise limited memory is proposed. In this algorithm, based on the system measurement noise and state noise statistical characteristics, the author adopted the online random weighting estimation. And the random weighting adaptive factor was adjusted to control the influence of the measurement noise and the state noise on the accuracy of the fiher. The simulation results showed that the accuracy of the proposed algo- rithm is superior to the traditional Sage adaptive Kalman filtering algorithm in the condition of the system with unknown noise statistics.

关 键 词：自适应Kalman滤波 随机加权估计 随机加权自适应滤波 深组合导航 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249.1