Research on Air Target Threat Assessment Technology Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.23-1323

Abstract

Abstract:

In order to realize the effective assessment of air combat targets, a deep learning-based air target threat assessment method is proposed. According to threat characteristics of the air target, the threat attributes of air target faced by electronic countermeasure operation are analyzed from the two perspectives of platform layer and equipment layer, the air target threat assessment index system is constructed, and the air target threat assessment index data set is established. Based on convolutional neural network, a residual structure is introduced to optimize the network, a threat assessment model is established, and the threat ranking of air targets is obtained by using the constructed index data set for training. Through simulation and experimental results, it is verified that the proposed threat assessment method has high accuracy, strong robustness, good applicability and effectiveness, which provides a new idea for threat assessment.

Key words: electronic countermeasure, threat assessment, deep learning, residual network, residual convolutional auto-encoder

CLC Number:

TP391.9

Jiang Dawei, Dong Yangyang, Zhang Lidong, Lu Xiao, Dong Chunxi. Research on Air Target Threat Assessment Technology Based on Deep Learning[J]. Journal of System Simulation, 2025, 37(3): 791-802.

Figures/Tables 13

Table 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 2

Aerial target original parameter information set

参数	目标1		目标2	目标3		目标4	目标5	目标6
威胁等级	3		2	1		2	3	0
类型	战斗机		轰炸机	预警机		电子战机	战斗机	直升机
最大瞬时转弯角速度/ $(°) / s$	13		5	7		9	17	5
最大瞬时加速度/ $g$	3.5		0.9	1.2		1.4	3	0.5
最大瞬时爬升率/( $m / s$ )	180		45	40		60	150	15
火力攻击能力	强		强	较差		一般	较强	一般
电磁干扰攻击能力	较差		较差	一般		强	一般	差
最大飞行高度/km	18		20	15		12	15	2
最大飞行速度/(m/s)	700		550	610		480	650	220
最大作战半径/km	1 500		2 000	1 800		1 200	1 550	500
RCS/ $m 2$	0.5		5	9		7	1	15
雷达主要功能	武器控制		武器控制	警戒引导		侦察	武器控制	其它
雷达最大探测距离/km	1 000		700	3 000		2 400	800	200
雷达距离分辨力/m	2		20	5		1	3	30
雷达方位分辨力/(°)	0.2		0.5	0.3		0.1	0.4	2
雷达速度分辨力(m/s)	2		20	10		5	8	30
雷达工作体制	相控阵		全相参	连续波		相控阵	单脉冲	连续波
雷达抗干扰措施	副瓣抑制、恒虚警、脉冲压缩	频率分集、极化可变		频率分集、极化可变	频率捷变、副瓣抑制、恒虚警		恒虚警、MTD	频率分集

Table 2

Table 3

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 4

Calculation Results of Evaluation Metrics for Aerial Target Regression Model

数据集	RMSE	MAPE	$R 2$
训练集	0.006 3	0.009 8	0.992 0
测试集	0.0082	0.0109	0.989 3

Table 4

Table 5

Comparison of Calculation Results for Model Evaluation Metrics

指标	RMSE	MAPE	$R 2$
R-CNN	0.006 3	0.009 8	0.992 0
ResNet18	0.008 2	0.010 9	0.989 3
CNN	0.013 9	0.018 9	0.962 4

Table 5

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评估指标	目标1	目标2	目标3	目标4	目标5	目标6
类型	1	1	0.6	0.8	1	0.4
机动能力	1	0.6	0.6	0.8	0.8	0.4
攻击能力	0.76	0.76	0.48	0.76	0.72	0.44
飞行能力	0.842	0.896	0.804	0.6	0.779	0.208
隐身能力	0.975	0.75	0.55	0.65	0.95	0.25
雷达主要功能	1	1	0.4	0.7	1	0.2
雷达最大探测距离	0.5	0.35	1	1	0.4	0.1
雷达分辨力	0.8	0.37	0.6	0.8	0.7	0.3
雷达抗干扰能力	0.7	0.6	0.9	1	0.7	0.3

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