基于EMD-DESN的无人机集群航迹目的地预测

doi:10.12305/j.issn.1001-506X.2024.01.33

Abstract

Abstract:

The unmanned aerial vehicle (UAV) cluster has various combat styles and complex movement modes, which makes it difficult to predict the cluster trajectory destination. To solve the above problems, we propose a UAV cluster trajectory destination prediction algorithm based on empirical mode decomposition (EMD) and deep echo state network (DESN) in this paper. In order to make the cluster movement model more realistic to simulate the UAV cluster combat process, we introduce the time-varying variance of heading error, and improves the virtual leadership term of Olfati-Saber cluster movement model. In order to deal with the non-stationary motion caused by the coordination within the cluster and the cluster heading error, EMD is introduced to reconstruct the UAV trajectory sequence. Considering the timing of the acquired trajectory, the sliding window structure is designed, and the DESN is used to predict the destination in different periods of the reconstructed trajectory. The simulation results show that the EMD-DESN algorithm proposed in this paper can predict the UAV cluster trajectory destination with higher accuracy than the basic DESN algorithm, and can achieve stable and correct prediction earlier.

Key words: unmanned aerial vehicle (UAV) cluster, destination prediction, deep echo state network (DESN), empirical mode decomposition (EMD), improved Olfati-Saber mode

CLC Number:

TN911.7

Xirui XUE, Shucai HUANG, Daozhi WEI, Jianfeng WU. Destination prediction of UAV cluster trajectory based on EMD-DESN[J]. Systems Engineering and Electronics, 2023, 46(1): 290-299.

Figures/Tables 16

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目的地	坐标(x, y)/m
D₁	(1 400, 2 040)
D₂	(1 960, 2 500)
D₃	(2 560, 3 360)
D₄	(2 880, 2 940)
D₅	(3 440, 2 680)

模态	v_x中心频率	v_y中心频率
IMF1	2.56	2.81
IMF2	1.06	1.04
IMF3	0.34	0.67
IMF4	0.19	0.31
IMF5	0.14	0.20
IMF6	0.14	0.14

算法	时刻/s
算法	20	40	60	80	100
DESN	276.3	423.3	216.9	209.0	88.3
EMD-ESN	288.3	432.0	306.0	329.0	70.6
EMD-DESN	235.6	392.9	162.3	180.6	83.5
EMD效果/%	14.7	7.2	25.2	13.6	5.4

算法	第1次	第2次
DESN	14.3	56.2
EMD-ESN	-	83.2
EMD-DESN	7.5	55.7

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Destination prediction of UAV cluster trajectory based on EMD-DESN

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