计算机工程Issue(1)：195-198,4.DOI:10.3969/j.issn.1000-3428.2014.01.0041
基于GP-SRCDKF的初始对准技术研究
Research on Initial Alignment Technology Based on GP-SRCDKF
摘要
Abstract
In order to improve the alignment time, initial alignment is carried on with large azimuth misalignment, and the nonlinear filtering methods are utilized. Therefore Gaussian Process regression Square Root Central Difference Kalman Filtering(GP-SRCDKF) is proposed, and which is taken Gaussian process regression into SRCDKF algorithm to get system regression model and noise covariance, regression model is taken instead of state equation and observation equation, and the corresponding noise covariance makes real-time adaptive adjustment, which not only overcomes the deficiencies that Extended Kalman Filtering(EKF) has low precision and needs to calculate the Jacobian matrix, but also solves the problems that traditional filter is limited by the uncertain system dynamic model and inaccurate noise covariance. Simulation results verify the effectiveness and superiority of the proposed algorithm.关键词
大方位失准角/捷联惯导/初始对准/高斯回归/高斯过程回归平方根中心差分卡尔曼滤波/自适应调整Key words
large azimuth misalignment/strapdown inertial navigation/initial alignment/Gaussian regression/Gaussian Process regression Square Root Central Difference Kalman Filtering(GP-SRCDKF)/adaptive adjustment分类
信息技术与安全科学引用本文复制引用
贾鹤鸣,宋文龙,牟宏伟,车延庭..基于GP-SRCDKF的初始对准技术研究[J].计算机工程,2014,(1):195-198,4.基金项目
国家自然科学基金资助项目(30972424)；中央高校基本科研业务费专项基金资助项目(DL13BB14) （30972424）
