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针对稳态编队在部分可辨条件下精细跟踪的难题,提出了一种基于迭代就近点(ICP)的稳态部分可辨编队精细跟踪算法。首先将ICP算法思想应用于编队成员拓扑的点航关联中,将k时刻的位置状态估计通过最近点循环迭代逼近k+1时刻的量测,在关联判决时采用双门限原则应对部分可辨所带来的漏观测问题,以提高关联时的容错性能;进而采用概率最近邻对漏观测航迹进行填补,以进一步保证跟踪的可靠性;最后,采用多模型法实现编队成员航迹滤波更新,以保证航迹的跟踪滤波精度。仿真结果表明,与现有的基于模版匹配的编队目标跟踪算法以及经典的多假设多目标跟踪算法相比,该算法具有较高的跟踪可靠性与精度,且在编队拓扑发生缓慢变化时具有更高的正确跟踪率。
Aiming at the problem of subtle tracking under steady states under partially identifiable conditions, an iterative near-point (ICP) -based steady-state partially-fielded tracking algorithm is proposed. First of all, the ICP algorithm is applied to the formation-point association of aircraft formation. The state estimate of position k is iterated through the nearest-point cycle iteration to the measurement of moment k + 1, and the double threshold principle is used to deal with the partially identifiable To improve the fault-tolerant performance of the association; then use the nearest-neighbor probability to fill the leakage observation track to further ensure the reliability of the tracking; Finally, the use of multi-model method to achieve the formation of member track filter update, In order to ensure the tracking accuracy of the track filter. The simulation results show that the proposed algorithm has higher tracking reliability and precision than the existing multi-target tracking algorithm based on template matching and the classic multi-hypothesis tracking algorithm, and has more advantages when the formation topology changes slowly High correct tracking rate.