量测随机丢失的非线性相关系统滤波算法  

EKF for Nonlinear Relevance Systems with Random Measurement Loss

作　　者：安喜彬 何兵 秦伟伟 林浩申 钱晓俊 AN Xi-bin HE Bing QIN Wei-wei LIN Hao-shen QIAN Xiao-jun(Department of Aerospace Engineering, Rocket Force University of Engineering, Xi＇an 710025, China Military Representative Office of Rocket Force in Liuzhou, Liuzhou 545012, China)

机构地区：[1]火箭军工程大学空间工程系,西安710025 [2]火箭军驻柳州军事代表室,广西柳州545012

出　　处：《电光与控制》2016年第10期18-20,53,共4页Electronics Optics & Control

基　　金：国家自然科学基金(61403399);陕西省基金(2015JQ6213)

摘　　要：针对非线性相关系统中量测数据随机丢失问题,研究了带有随机量测数据丢失且带有相关噪声的扩展卡尔曼滤波算法。通过引入相关系数和服从伯努利分布的传输系数的方法,提出了带相关噪声的量测数据随机丢失EKF。最后,将所提算法应用于空间非合作目标的跟踪问题,仿真验证了算法的有效性。To the problem of random measurement loss in nonlinear systems, the Extend Kalman Filter （EKF） with random measurement loss and with relevance noise is studied. By using correlation coefficient and the transmission factor following Bemouli distribution, an EKF with random measurement loss is proposed for nonlinear systems with relevance noise. Finally, the algorithm is applied in tracking space non- collaborate objects, and its validity is proved.

关 键 词：卡尔曼滤波 EKF 相关噪声 量测丢失 

分 类 号：V412.4[航空宇航科学与技术—航空宇航推进理论与工程]