MFA-Net：一种面向复杂对抗环境的反舰导弹智能识别多模态自适应融合网络

doi:10.12305/j.issn.1001-506X.2026.03.16

Abstract

Abstract:

Aiming at the problems of blurred features, strong decoy deception, and insufficient robustness of traditional recognition algorithms faced by anti-ship missiles target recognition in complex adversarial environments, a multimodal adaptive fusion network （MFA-Net） is proposed. The model employs parameter non-shared branches to extract heterogeneous features from radar, infrared, and electronic support measures, and achieves cross-modal adaptive fusion through a dual-dimensional channel–space attention mechanism. By introducing an adversarial training strategy based on the momentum iterative method, the model enhances its intrinsic robustness within a minimax optimization framework, thereby improving decision stability under jamming conditions. A nonlinear comprehensive recognition effectiveness index （CREI） is constructed by integrating the Macro-F1 score, anti-jamming robustness, and inference timeliness. Experimental results demonstrate that the MFA-Net achieves a CREI of 0.853 1, significantly outperforming several comparative models. Sensitivity analysis of jamming intensity further validated the performance stability of the model under different levels of adversarial attacks.

Key words: deep learning, adaptive fusion, dual-dimensional attention, intelligent recognition, momentum iterative method

CLC Number:

TJ 760

Long ZHANG, Lianhong ZHU, Bo YANG, Zhen LEI, Xuanming FENG. MFA-Net: a multimodal adaptive fusion network for intelligent recognition of anti-ship missiles in complex adversarial environments[J]. Systems Engineering and Electronics, 2026, 48(3): 894-907.

Figures/Tables 13

Fig.1

Table 1

Table 2

Multimodal feature system for intelligent recognition of anti-ship missiles"

传感器模态	特征类别	特征名称	维度	量纲	军事含义与解释
雷达	能量与散射	RCS	1	dBsm	目标大小与隐身性的核心度量
	能量与散射	RCS起伏标准差	1	dB	目标散射稳定性，用于识别舰型
	运动特性	径向速度	1	m/s	目标接近/远离速度，火控解算基础
	运动特性	径向加速度	1	m/s²	目标机动意图判断依据
	高分辨像	距离像主峰幅度	5	V	对应舰艇突出结构，是型号识别的关键
		距离像质心	1	m	散射中心分布位置
		距离像方差	1	m²	散射分布离散度
		距离像偏度	1	—	散射分布不对称性
	微动特性	微多普勒谱熵	1	—	量化微动复杂性，有效鉴别真假目标
	极化特性	极化熵	1	—	区分目标形状复杂度与材质，抗欺骗干扰
	极化特性	共极化/交叉极化比	1	dB	进一步鉴别目标材质与海面杂波
红外	辐射强度	最大辐射强度	1	W/sr	最强热源温度，决定发现距离
		平均辐射强度	1	W/sr	目标整体热轮廓强度
		辐射对比度	1	—	目标与海背景的分离难易度
	统计分布	辐射强度标准差	1	W/sr	热分布的均匀性
	统计分布	辐射熵	1	—	热模式复杂性，真目标熵值高
	形状轮廓	目标占空比	1	—	预估目标大小
		目标长宽比	1	—	区分舰型（如航母与驱护舰）
		轮廓紧致度	1	—	形状规则度，诱饵通常更规则
	热源分析	热源数量	1	—	关键判别指标，真实舰船多稳定热源
		最强热源位置X	1	pixel	识别舰艇朝向
		最强热源位置Y	1	pixel	识别舰艇朝向
	光谱特性	中波/长波红外强度比	1	—	鉴别真假目标的核心特征
	纹理特征	中波红外纹理特征均值	1	—	描述热图细腻程度，抗干扰
ESM	基本参数	射频	1	MHz	识别雷达频段，判断威胁等级
		脉冲重复间隔	1	μs	识别雷达模式与型号
		脉冲宽度	1	μs	判断雷达功能与作用距离
		到达角AOA	1	°	对辐射源进行定向
		信号强度	1	dBm	估算辐射源距离
	调制样式	频率调制类型	1	—	识别雷达体制与抗干扰能力
	调制样式	脉冲调制类型	1	—	识别雷达编码方式
	指纹特征	载频稳定度	1	ppm	辐射源个体识别的关键指纹
		脉冲上升时间	1	ns	硬件电路固有特征，难以模仿
		脉冲下降时间	1	ns	同上，用于抗欺骗干扰
		脉冲顶部起伏	1	%	表征发射机功率稳定性的指纹
		频谱旁瓣特性	1	dBc	表征发射机频谱纯度的指纹

Table 2

Table 3

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 4

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模态	输入维度	网络深度	隐藏层维度序列	输出维度
$ {m}1 $	$ {{d}}_{{m}1} $	$ {L} $	$ \left[{d}_{{h}}^{\left(1\right)},{d}_{{h}}^{\left(2\right)},...,{d}_{{h}}^{\left({L}\right)}\right] $	$ {{d}}_{{h}} $
$ {m}2 $	$ {{d}}_{{m}2} $	$ {L} $	$ \left[{d}_{{h}}^{\left(1\right)},{d}_{{h}}^{\left(2\right)},...,{d}_{{h}}^{\left({L}\right)}\right] $	$ {{d}}_{{h}} $
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
$ {m}{M} $	$ {{d}}_{{m}{M}} $	$ {L} $	$ \left[{d}_{{h}}^{\left(1\right)},{d}_{{h}}^{\left(2\right)},...,{d}_{{h}}^{\left({L}\right)}\right] $	$ {{d}}_{{h}} $

模型	Macro-F1	鲁棒性	延迟/ms	CREI	军事意义解读
CNN	0.818 7	0.247 8	2.302 3	0.589 8	响应迅捷，易于部署；然其抗干扰能力极差，在强电磁对抗中易被瘫痪，仅适用于和平环境或低威胁侦察
ResNet-1D	0.725 7	0.303 2	4.993 3	0.581 3	虽具备一定深度特征学习能力，但决策迟缓且易受欺骗，在高速突防场景下难以胜任，面临被战场淘汰的风险
SE-ResNet-1D	0.757 3	0.445 0	5.830 5	0.663 3	通过内在注意力机制提升了抗干扰韧性，但实时性是其致命短板，适合用于后方情报中心进行深度分析
MulT	0.919 4	0.514 2	2.930 1	0.762 4	识别精度顶尖，在多模态融合上表现出色；但其鲁棒性不足以应对体系化、高烈度电子对抗，是需重点保护的尖刀力量
MFA-Net	0.909 9	0.722 8	2.272 4	0.853 1	在精度、抗干扰与实时性上取得最佳平衡，是复杂电磁环境下可靠决策的基石，能显著提升导弹突防概率与体系作战效能

模型配置	Macro-F1	鲁棒性	CREI
A	0.841 2	0.523 5	0.701 5
B	0.881 5	0.385 0	0.674 3
C	0.896 8	0.655 1	0.802 1
D	0.909 9	0.722 8	0.853 1

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MFA-Net: a multimodal adaptive fusion network for intelligent recognition of anti-ship missiles in complex adversarial environments

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