一种基于卡尔曼滤波的雷达航迹数据融合算法应用于某型洲际弹道导弹

J. Ferrante
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引用次数: 6

摘要

本文开发了一种基于卡尔曼滤波的方法,用于融合来自两个独立相控阵雷达传感器的轨迹数据,并将其应用于选定的洲际弹道导弹案例,以证明在单个雷达上位置和速度估计的潜在增强。与基于稳态滤波性能的理论评估相比,卡尔曼滤波方法的性能提高幅度在理论预测的7%以内。理论评估表明,在两个雷达的假设偏差误差下,位置精度提高33%,速度精度提高29%。在相同偏差假设下,仿真结果表明位置精度提高了29%,速度精度提高了22%。相对于具有两倍波束宽度和与第二“融合”雷达相同灵敏度的雷达,计算了改进。据推测,这两个雷达将同时部署在洲际弹道导弹飞行的末端区域。
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A Kalman filter-based radar track data fusion algorithm applied to a select ICBM case
A Kalman filter-based approach for fusing track data from two separate phased array radar sensors is developed and applied to a select ICBM case to demonstrate the potential enhancement of position and velocity estimates over a single radar. When compared to a theoretical assessment based on steady state filter performance, the Kalman filter approach yielded performance enhancements within 7% of theoretical prediction. The theoretical assessment indicated a 33% improvement in position accuracy and a 29% improvement in velocity accuracy for an assumed bias error in both radars. The simulation yielded a 29% improvement in position accuracy and a 22% improvement in velocity accuracy with the same bias assumption. The improvement was computed relative to the radar with twice the beamwidth and the same sensitivity as the second "fused" radar. The two radars were assumed to be collocated at the terminal area of ICBM flight.
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