张云飞 , 林德福 , 郑多 , 等 . 多目标时空同步协同攻击无人机任务分配与轨迹优化 [J ] . 兵工学报 , 2021 , 42 ( 7 ): 1482 - 1495 .

ZHANG Y F , LIN D F , ZHENG D , et al. Task allocation and trajectory optimization of UAV for multi-target time-space synchronization cooperative attack [J ] . Acta Armamentarii , 2021 , 42 ( 7 ): 1482 - 1495 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.07.016 http://doi.org/10.3969/j.issn.1000-1093.2021.07.016 A task allocation and trajectory optimization algorithm for multi-target attack is proposed for the coordinated attack task of distributed UAVs attacking multiple targets in a complex battlefield environment. The typical multi-target strike mission scenarios and UAV models are established. A search map with no-fly zones as nodes is constructed based on Delaunay triangle theory. A^* algorithm is used to achieve the least threatening single-aircraft path search. In terms of UAV dynamics constraints and minimum energy loss, the time adjustment factor is introduced and the distributed UAV time-space synchronization trajectory optimization method based on Bezier curve is adopted to obtain the optimized trajectory for simultaneous attack on multiple targets. A trajectory tracking controller was designed for simulation track of pre-planned trajectory. The simulated results show that the proposed multi-target attack task allocation and trajectory optimization method can achieve multi-angle, time-space synchronization, and distributed coordinated attack on multiple targets, and it has strong robustness against interference such as noise and wind gusts.

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邹汝平 , 刘建书 . 基于概率假设密度滤波与无迹Kalman滤波的多目标跟踪与识别 [J ] . 兵工学报 , 2020 , 41 ( 8 ): 1502 - 1508 . DOI: 10.3969/j.issn.1000-1093.2020.08.004 http://doi.org/10.3969/j.issn.1000-1093.2020.08.004 为提高导引头末制导阶段抗干扰能力，针对典型的欺骗式距离-速度联合拖引干扰模型，研究了基于概率假设密度（PHD）滤波的多目标跟踪与基于无迹Kalman滤波（UKF）的多目标识别技术。为说明导引头目标识别原理，给出了距离-速度联合拖引干扰模型；根据导引头测量原理，通过导引头框架角、导弹-目标相对距离、径向速度建立系统跟踪模型，给出了基于PHD滤波的多目标跟踪与基于UKF的多目标识别的基本原理；基于典型的目标运动模型（匀速直线与匀速转弯模型），针对目标施加的4次距离-速度联合拖引干扰，采用目标跟踪结果以及估计的目标速度和加速度信息进行多目标跟踪与识别分析，能够很快实现真假目标识别。仿真实验结果表明，利用PHD滤波与UKF信息能够有效实现对距离-速度拖引干扰下的多目标跟踪与识别。

ZOU R P , LIU J S . Multi-target tracking and recognition technology based on PHD and UKF [J ] . Acta Armamentarii , 2020 , 41 ( 8 ): 1502 - 1508 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.08.004 http://doi.org/10.3969/j.issn.1000-1093.2020.08.004 For the typical range-velocity simultaneous pull-off jamming model, the multi-target tracking technology based on probability hypothesis density (PHD) filter and the multi-target recognition technology based on unscented Kalman filter (UKF) are studied to improve the anti-jamming capability of seeker in terminal guidance phase. A range-velocity simultaneous pull-off jamming model is established to explain the principle of target recognition. According to the principle of seeker measurement, a system tracking model is established， in which the frame angle of seeker, missile-target relative distance, radial velocity, elevatiom angle and azimuth angle are considered. The basic principle of multi-target tracking based on PHD filter and multi-target recognition based on UKF filter is presented. Finally, in the typical target motion model (uniform rectilinear and uniform turning model), the target velocity and acceleration information estimated by filtering can be used to identify real and false targets quickly in view of the four target range-velocity simultaneous pull-off jammings. The simulated results show that the PHD and UKF filters presented in this paper can effectively realize the multi-target recognition and tracking under range-velocity simultaneous pull-off jamming, and verify the feasibility and effectiveness of the proposed algorithm.

张哲 , 吴剑 , 代冀阳 , 等 . 基于改进A * 算法的多无人机协同战术规划 [J ] . 兵工学报 , 2020 , 41 ( 12 ): 2530 - 2539 . DOI: 10.3969/j.issn.1000-1093.2020.12.019 http://doi.org/10.3969/j.issn.1000-1093.2020.12.019 多无人机协同作战是未来无人机作战方式的重要发展趋势。为增强多无人机系统的任务执行能力，提高系统整体作战效能并实现高效资源分配和调度，提出一种基于改进A^*算法的多无人机协同战术规划方法。按照离线规划和重规划两方面，设计战役层和战术层的作战目标迭代优化方案；建立编队协同作战的数学模型，以编队成员间的时间协同和碰撞协同代价为变量，得到多约束条件下的综合编队目标函数；结合多层变步长搜索策略和单步扩展的搜索方式，基于改进A^*算法，用于求解复杂战场环境下的多无人机编队协同作战航路。分别利用改进A^*算法和传统A^*算法进行对比仿真实验。仿真结果表明，多无人机协同战术规划方法能够较好地完成作战任务，改进A^*算法能够获得更优的航路，从而验证了所提算法的有效性。

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