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 Bernouli 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.