空间目标的数据处理研究

Study of Radar Data Processing in Space Objects Tracking

随着人造卫星的日益智能化,精确跟踪卫星的坐标和速度也越来越重要。然而,目前对空间目标的跟踪滤波研究主要集中在航天、天体测量等领域,在雷达数据处理领域则鲜有研究。本文针对雷达数据处理的特点,采用协议地球坐标系下的卫星运动模型分析比较了最小二乘法、扩展卡尔曼滤波法和不敏卡尔曼滤波法的性能。研究结果发现扩展卡尔曼滤波的费效比最好,适合用于空间目标的跟踪滤波。

With the ever increasing sophistication of satellites in Earth orbit, the precise tracking the position and velocity of the satellite is extremely important. However, studies on tracking space objects mainly focus on infields such as spaceflight, astrophysics, while studies are rare in radar data processing fields. Given radars＇ characteristics, this article compares three different algorithms, least square method （LS）, extended Kalman filter （EKF） and unscented Kalman filter （UKF）, in space targets tracking. The comparison concludes that EKF is the most effective rriethod, which is better in space objects real time tracking.