基于模糊迭代均方根容积卡尔曼滤波的天基非合作目标跟踪  

Non-cooperative space target tracking based on fuzzy iterative square-root cubature Kalman filter

作　　者：岳聪[1] 薄煜明[1] 吴盘龙[1] 田梦楚[1] 陈志敏[1] 

机构地区：[1]南京理工大学自动化学院,南京210094

出　　处：《中国惯性技术学报》2017年第3期395-398,404,共5页Journal of Chinese Inertial Technology

基　　金：国家自然科学基金(61473153);江苏省"六大人才高峰"项目(2015-XXRJ-006);航空科学基金(2016ZC59006)

摘　　要：针对非合作航天器相对导航中测量噪声不确定的问题,提出了一种模糊迭代均方根容积卡尔曼滤波算法,实现对非合作目标相对状态的测量。该算法利用容积点均方根迭代策略和模糊推理系统实时调整改进容积卡尔曼滤波的量测噪声协方差阵权值,修正量测噪声协方差阵,使其接近真实噪声值,从而提高目标跟踪算法的自适应能力,提高了滤波精度。通过建立数学仿真模型,分别采用扩展卡尔曼滤波、容积卡尔曼滤波以及模糊迭代均方根容积卡尔曼滤波进行跟踪仿真,仿真结果表明,与标准容积卡尔曼滤波相比,该改进算法能够提高13.17%的跟踪精度。In view of the problem that the statistic characteristics of the measurement noise is uncertain in the relative navigation of non-cooperative spacecraft, a fuzzy iterative RMS（root mean square） cubature Kalman filtering algorithm is proposed to realize the relative state measurement of the non-cooperative target. The measurement noise covariance of cubature Kalman filtering is adjusted in real-time by using the fuzzy inference system to make it closer to the real measurement covariance. And a cubature point RMS iteration strategy is utilized to overcome the limitations of the traditional sampling based on Gaussian approximation and improve the filtering precision. Simulation tracking is conducted with different filtering algorithms, and the results show that the improved algorithm can improve the tracking accuracy by 13.17% compared with the standard cubature Kalman filter.

关 键 词：非合作目标 容积卡尔曼滤波 模糊推理系统 自适应滤波 目标跟踪 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]