基于改进强跟踪ASCKF算法的SINS/GNSS组合对准方法  

SINS/GNSS Integrated Alignment Method Based on Improved Strong Tracking ASCKF Algorithm

作　　者：刘轶 程旭红 程建华[3] IIU Yi;CHENG Xu hong;CHENG Jian hua(Naval Aviation Nepresentative Office in Beijing Area,Beijing 100073,China;Broadcast Department,Yantai Broadcast and Television Information Network Itd.,Yantai 2641000,China;School of Automation,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]海军驻北京地区航空军事代表室,北京100073 [2]烟台广电信息网络中心广电技术部,烟台264000 [3]哈尔滨工程大学自动化学院,哈尔滨150001

出　　处：《导航定位与授时》2018年第5期13-19,共7页Navigation Positioning and Timing

基　　金：国家自然科学基金重点项目(61633008);中央高校基本科研业务费(HEUCFX41309)

摘　　要：大方位失准角下的SINS/GNSS组合对准系统呈非线性,采用传统的卡尔曼滤波方法进行初始对准易导致对准精度下降甚至滤波发散。基于此,提出了一种基于改进强跟踪自适应平方根容积卡尔曼滤波算法的组合对准方法。该方法采用QR分解求取协方差的分解因子,并在状态预测方差阵的平方根更新中引入多重渐消因子调整滤波增益;同时,基于Sage-Husa自适应滤波,引入改进的时变噪声估计器实时估计噪声的统计特性。仿真结果表明,采用改进的滤波算法进行大方位失准角下的组合对准,对准精度明显提高。As the SINS/GNSS integrated alignment system with a large azimuth misalignment ang le is non linear and the traditional Kalman filter method for initial alignment would lead to poor a lignment accuracy or even filtering divergence, a SINS/GNSS integrated alignment method based on the improved strong tracking Adaptive Square root Cubature Kalman Filter （ASCKF） algori thin is proposed in this paper. The proposed method directly adopts QR factorization to get the factor of covariance matrix and introduces the multiple fading factors to adjust the filtering gain during the square root updating of state prediction covariancematrix. Combined with Sage Husaa daptive filter, an improved noise statistics estimator is designed to estimate noise statistics in real time. Simulation results show that the proposed algorithm can increase the accuracy of integrated alignment with a large azimuth misalignment angle.

关 键 词：组合对准 大方位失准角 平方根容积卡尔曼滤波 多重渐消因子 Sage-Husa自适应滤波 

分 类 号：U666.1[交通运输工程—船舶及航道工程]