基于IMM-UKF的网络化作战目标跟踪算法

发布时间：2018-03-09 08:00

本文选题：网络化作战　切入点：机动目标跟踪　出处：《火力与指挥控制》2017年10期 　论文类型：期刊论文

【摘要】：针对网络化作战中单探测节点对机动目标的运动模型不确定性导致滤波精度低,为提高目标跟踪的稳定性和精确性,提出了多节点探测跟踪算法。基于网络体系中信息的共享需求,建立网络探测节点的目标跟踪模型;通过网络探测节点目标跟踪需求和实战要求发现目标经常有多种运动状态并存现象,而单一模型的滤波器不能满足对机动目标跟踪性能的要求。因此,探测节点采用了基于交互式多模型(IMM)的无迹卡尔曼滤波(UKF)算法进行状态估计,融合中心将各节点发送的目标状态估计融合后进行状态估计,有效地降低了目标机动造成的模型误差,提高了跟踪性能。仿真结果表明,所提出的算法提高了网络节点对机动目标的跟踪精度,并且收敛速度快,有较强的鲁棒性和实用性。
[Abstract]:In order to improve the stability and accuracy of target tracking, the uncertainty of moving model between single probe node and maneuvering target leads to low filtering accuracy in networked warfare. A multi-node detection and tracking algorithm is proposed. Based on the requirement of information sharing in the network system, the target tracking model of the network detection node is established. Through the network detection node target tracking requirements and actual combat requirements, it is found that the target often has a variety of moving state coexistence phenomenon, but the single model filter can not meet the requirements of maneuvering target tracking performance. The detection node uses the unscented Kalman filter UKF algorithm based on interactive multi-model IMM to estimate the state, and the fusion center fuses the target state estimation sent by each node to estimate the state. The simulation results show that the proposed algorithm can improve the tracking accuracy of maneuvering targets, and the convergence speed is fast, and the algorithm has strong robustness and practicability.
【作者单位】：空军工程大学航空航天工程学院;
【基金】：国家自然科学基金资助项目(61472441)
【分类号】：E91;TN713

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