基于通信域和雷达域融合特征的无人机集群类型识别算法

doi:10.12305/j.issn.1001-506X.2023.12.03

Abstract

Abstract:

At present, the unmanned aerial vehicle (UAV) type recognition algorithms only realize the identification of a single UAV type through the signal characteristics of the communication domain or radar domain, and there are problems such as low recognition accuracy. This paper proposes a UAV swarm type recognition algorithm based on the fusion characteristics of communication signals and radar signals. Firstly, the high-order cumulant and instantaneous feature statistics of the swarm communication signal are extracted, and the radar track features are fused to construct the UAV swarm feature matrix. Secondly, an improved feature selection algorithm-secondary screening of neighbourhood components analysis (SSNCA) is proposed to reduce the dimensionality of the fusion feature matrix. Finally, a sparse autoencoder network is used for swarm type identification. The simulation results show that the algorithm significantly reduces the dimension of the swarm feature matrix (only 27% of the original matrix dimension). At the same time, when the signal-to-noise ratio is 0 dB, the correct rate of identifying five swarm types can reach 88%.

Key words: unmanned aerial vehicle (UAV) swarm type identification, feature selection, high-order cumulant, instantaneous feature statistics, radar trajectory

CLC Number:

TN911

Shuheng ZHANG, Ruping ZHAI, Yongkai LIU. Identification of UAV swarm type based on fusion features of communication and radar domain[J]. Systems Engineering and Electronics, 2023, 45(12): 3734-3742.

Figures/Tables 13

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References 21

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结构或参数	设置
输入层	14/46/51
隐藏层	(10, 8)/(30, 20)
输出层	5
激活函数	Sigmoid, Softmax
优化算法	L-BFGS
稀疏系数(ρ)	0.1
惩罚系数(β)	3

集群类型	集群内通信用户数量	通信信号调制方式	集群航迹类型
A	2	BPSK	航迹类型1
B	2	QPSK	航迹类型2
C	2	8PSK	航迹类型3
D	2	16QAM	航迹类型4
E	2	64QAM	航迹类型5

雷达参数	变量符号	数值
频率/GHz	f₀	10
带宽/MHz	B₀	15
脉冲重复周期/μs	PRP	120
快时间点数	N₁	256
慢时间点数	N₂	128

维度	变量符号	数值
距离/m	d	[200, 1 200]
方位/(°)	α	[0, 30]
速度/(m·s^-1)	v	[10, 30]
高度/m	h	[30, 100]

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Identification of UAV swarm type based on fusion features of communication and radar domain

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