基于改进粒子滤波的无人机编队协同导航
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UAV Formation Cooperative Navigation Based on Improved Particle Filter
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    摘要:

    针对主从式无人机编队协同导航问题,建立主从无人机编队的运动模型,并将闪烁噪声加入到观测模型 中,使用粒子滤波算法进行仿真实验,使实验结果更具现实性。针对粒子滤波在重采样过程中出现的粒子贫化现象, 在粒子权重归一化过程中引入权重影响因子,给出理论证明,并与标准粒子滤波算法进行对比。仿真结果表明:在 闪烁噪声下,改进后的粒子滤波使主从式无人机编队导航精度明显提高,具有一定的实用价值。

    Abstract:

    Aiming at the co-navigation problem of master-slave UAV formation, the motion model of master-slave UAV formation is established, and the flicker noise is added to the observation model. The particle filter algorithm is used to simulate the experiment, which makes the experimental result more realistic. Aiming at the phenomenon of particle depletion in particle re-sampling process, the weight influence factor is introduced in the process of particle weight normalization, and the theoretical proof is given, and compared with the standard particle filter algorithm. The simulation results show that under the flicker noise, the improved particle filter makes the navigation precision of the master-slave UAV formation significantly improved, which has certain practical value.

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引用本文

邓伟栋.基于改进粒子滤波的无人机编队协同导航[J].,2020,39(06).

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历史
  • 收稿日期:2020-02-20
  • 最后修改日期:2020-04-03
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  • 在线发布日期: 2020-06-03
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