航天器对地面运动目标的复合无源定位研究

Research on Double Satellites Hybrid Passive Location of Ground Moving Target

研究双星航天器对地面运动目标的测向时差复合无源定位问题。双星航天器对地面运动目标的无源定位过程中,初始状态估计误差大,量测方程非线性,而工程上广泛应用的扩展卡尔曼滤波(EKF)精度低、易发散。针对这些问题,采用一种基于无迹卡尔曼滤波(UKF)的定位算法进行复合无源定位。算法用目标运动学参数作为状态量,由双星系统的无源定位过程获得量测方程,建立非线性滤波模型。在状态方程为线性方程且测量噪声为加性高斯噪声的条件下,推导了无迹卡尔曼滤波算法。蒙特卡洛仿真结果表明:在双星航天器对地面运动目标的测向时差复合定位中,无迹卡尔曼滤波算法是有效的,与扩展卡尔曼滤波相比,在定位精度、滤波稳定性方面显得更好。

The paper studied the problem of double satellites system locating the moving target on the ground by direction-finding and the time difference of arrival（TDOA） hybrid passive location technology. A new kind of direction-finding and TDOA hybrid location algorithm based on Unscented Kalman Filter（UKF） was presented when the Extended Kalman Filter（EKF） applied widely in engineering suffers the problem of low precision and mostly divergent characteristics because of the nonlinear measure equation and large original state estimating error in double satellites hybrid location. Using the algorithm, we used target kinematics parameters as system state variables and obtained the system measure equation from the progress of double satellites passive location, and the nonlinear filtering model was constructed. The Unscented Kalman Filter was derived when state equation is linear and measure error is addictive Gaussian noise. The results of Monte Carlo simulation show that the Unscented Kalman Filter is effective in the direction-finding and TDOA hybrid location of double satellites system. Compared with the Extended Kalman Filter, the Unscented Kalman Filter performs better in precision and stability.