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多弹道目标跟踪伴随着速度快、密集程度高以及测量误差大等因素,在跟踪过程中发生航迹中断是一个很普遍的现象。在未知导弹任何先验信息前提下如何将中断前后的航迹片段进行关联,提高航迹的连续性已成为亟待解决的难题。针对该问题提出了一种实时的离散最优关联算法,它利用速度信息对中断前后弹道外推航迹进行粗关联;将对数似然函数作为关联代价函数,采用拍卖算法完成关联代价函数的二维全局最优分配实现细关联并提高了关联概率;对关联上的航迹片段利用“窗口”统计最小误差方法进行优化,提高了整个航迹的连续性。仿真结果表明该算法能够将航迹平均周期提高1倍,在航迹中断期间对比最小二乘方法能够较大幅度地减小跟踪位置和速度的均方根(RMS)误差。
Multi-trajectory target tracking with the speed, high intensity and measurement error and other factors, the track occurred in the track interruption is a very common phenomenon. How to correlate the track segments before and after the interruption on the premise of any prior information of the unknown missile to improve the continuity of the track has become an urgent problem to be solved. Aiming at this problem, a real-time discrete optimal correlation algorithm is proposed, which uses the velocity information to roughly correlate the trajectory extrapolation before and after the interruption. Taking the log-likelihood function as the correlation cost function and the auction algorithm as the correlation cost function The two-dimensional global optimal allocation achieves the fine association and improves the association probability. The track segment on the association is optimized by the “window” statistical minimum error method to improve the continuity of the entire track. The simulation results show that the proposed algorithm can double the average track period and contrast the least squares method to reduce the root mean square error (RMS) of tracking position and velocity greatly.