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在利用地心坐标系的空间匹配算法(ECEFA)实现不同观测雷达空间数据同步的基础上,建立了机动目标跟踪的“当前”统计(CS)模型,通过Kalman滤波,实现了机动目标的有效跟踪.针对混合量测阶段,提出了集中式融合算法,实现了航迹的有效估计;对于机动目标观测断裂问题,提出了航迹粘联算法,有效地提高了航迹的连续性,降低目标状态估计误差.针对所提供的机动目标本身特征,提出了基于修正Hough变换的机动目标航迹起始策略,采用基于概率数据关联(PDA)的机动目标数据关联算法,实现了各目标的有效数据关联,确定了最终的两条航迹;最后,分别从目标的机动跟踪模型与雷达自身属性的角度出发,制订了机动目标的最佳逃逸策略.
Based on the spatial matching algorithm of geocentric coordinate system (ECEFA), the “current” statistic (CS) model of maneuvering target tracking is established. Based on Kalman filtering, the maneuvering target Aiming at the mixed measurement phase, a centralized fusion algorithm is proposed to realize the effective estimation of the track. For the observation and fracture of the maneuvering target, a track-sticking algorithm is proposed, which effectively improves the continuity of the track and reduces Target state estimation error.According to the characteristics of the provided maneuvering targets, a maneuvering target trajectory starting strategy based on modified Hough transform is proposed, and the maneuvering target data association algorithm based on probability data association (PDA) Finally, the best escape strategy of maneuvering target is formulated from the perspectives of maneuver tracking model and radar’s own attributes respectively.