Track correlation algorithm based on CNN-LSTM for swarm targets

doi:10.23919/JSEE.2024.000033

Abstract

Abstract:

The rapid development of unmanned aerial vehicle (UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation. In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets. Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM) Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation, while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Key words: track correlation, correlation accuracy rate, swarm target, convolutional neural network (CNN), long short-term memory (LSTM) neural network

Jinyang CHEN, Xuhua WANG, Xian CHEN. Track correlation algorithm based on CNN-LSTM for swarm targets[J]. Journal of Systems Engineering and Electronics, 2024, 35(2): 417-429.

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Swarm size	Formation
Swarm size	“One-line”	“V-line”	“Diamond”	“Arrow”
8	99.34	98.42	98.51	98.12
80	95.41	94.88	94.71	94.34
400	91.30	90.03	89.86	88.08
800	84.76	83.14	83.72	81.89

Swarm formation style	End-to-end track association algorithm	Track association algorithm based on machine learning	Algorithm proposed in this paper
“One-line” formation	82.81	86.32	91.30
“V-line” formation	81.33	85.82	90.03
“Diamond” formation	70.87	84.43	89.86
“Arrow” formation	79.01	82.79	88.08

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