无人机蜂群轨迹预测研究

V279%V249%TP183; 传统防空火控算法中的轨迹预测模型无法对复杂的无人机蜂群进行有效轨迹预测,而现有针对无人机机动轨迹的预测研究通常只考虑单个无人机,模型量级过大.为了准确且快速地预测无人机蜂群轨迹,提出一种面向蜂群的轨迹预测方法.在获得蜂群轨迹后,首先采用具有噪声的基于密度的聚类(DBSCAN)方法对其进行聚类,判断出蜂群中各个无人机的类别;然后基于分形算法,判断无人机轨迹是简单轨迹还是复杂轨迹;最后采用卡尔曼滤波进行简单轨迹预测,基于长短期记忆(LSTM)网络方法进行复杂轨迹的预测.结果表明:本文提出的无人机蜂群轨迹预测方法的预测误差远小于仅采用卡尔曼滤波方法的预测误差,且预测...

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Published in:航空工程进展 Vol. 14; no. 3; pp. 69 - 76
Main Authors: 张根源, 林智伟, 唐旭, 雷凯文
Format: Journal Article
Language:Chinese
Published: 西北机电工程研究所 一部, 咸阳 712099 01.06.2023
Subjects:
LSTM网络
无人机蜂群
分形算法
卡尔曼滤波
轨迹预测
ISSN:1674-8190
Online Access:Get full text
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Abstract V279%V249%TP183; 传统防空火控算法中的轨迹预测模型无法对复杂的无人机蜂群进行有效轨迹预测,而现有针对无人机机动轨迹的预测研究通常只考虑单个无人机,模型量级过大.为了准确且快速地预测无人机蜂群轨迹,提出一种面向蜂群的轨迹预测方法.在获得蜂群轨迹后,首先采用具有噪声的基于密度的聚类(DBSCAN)方法对其进行聚类,判断出蜂群中各个无人机的类别;然后基于分形算法,判断无人机轨迹是简单轨迹还是复杂轨迹;最后采用卡尔曼滤波进行简单轨迹预测,基于长短期记忆(LSTM)网络方法进行复杂轨迹的预测.结果表明:本文提出的无人机蜂群轨迹预测方法的预测误差远小于仅采用卡尔曼滤波方法的预测误差,且预测时间小于仅采用LSTM网络方法预测的时间,可以较为准确地预测蜂群中不同集群无人机的轨迹,能够为反无人机蜂群火控解算提供参考.
AbstractList V279%V249%TP183; 传统防空火控算法中的轨迹预测模型无法对复杂的无人机蜂群进行有效轨迹预测,而现有针对无人机机动轨迹的预测研究通常只考虑单个无人机,模型量级过大.为了准确且快速地预测无人机蜂群轨迹,提出一种面向蜂群的轨迹预测方法.在获得蜂群轨迹后,首先采用具有噪声的基于密度的聚类(DBSCAN)方法对其进行聚类,判断出蜂群中各个无人机的类别;然后基于分形算法,判断无人机轨迹是简单轨迹还是复杂轨迹;最后采用卡尔曼滤波进行简单轨迹预测,基于长短期记忆(LSTM)网络方法进行复杂轨迹的预测.结果表明:本文提出的无人机蜂群轨迹预测方法的预测误差远小于仅采用卡尔曼滤波方法的预测误差,且预测时间小于仅采用LSTM网络方法预测的时间,可以较为准确地预测蜂群中不同集群无人机的轨迹,能够为反无人机蜂群火控解算提供参考.
Author 张根源
林智伟
雷凯文
唐旭
AuthorAffiliation 西北机电工程研究所 一部, 咸阳 712099
AuthorAffiliation_xml – name: 西北机电工程研究所 一部, 咸阳 712099
Author_FL LIN Zhiwei
TANG Xu
LEI Kaiwen
ZHANG Genyuan
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Keywords LSTM网络
无人机蜂群
分形算法
卡尔曼滤波
轨迹预测
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