基于雷达-红外成像特征级融合的角反射器智能识别算法

doi:10.12382/bgxb.2024.0501

摘要/Abstract

摘要：

海面角反射器具有极强的雷达回波特性,其形成的假目标在时域持续干扰,制造虚假态势,给导引头的精准打击带来很大挑战。针对该问题,利用红外传感器不受角反射器干扰的优势,提出一种基于雷达-红外特征级融合的多目标场景下角反射器智能识别算法。通过YOLOv8网络对红外图像的目标干扰进行初次判别,高置信度目标图像直接输出识别结果;对低置信度目标图像单独裁剪,利用雷达-红外角度信息进行目标关联并提取雷达特征;将雷达特征与红外图像作为双通道融合网络的输入,实现对低置信度目标的二次判别。经实测数据验证,所提方法的识别准确率在96%以上,所做工作对角反射器干扰识别具有参考意义。

关键词: 角反射器, 目标识别, 雷达红外复合制导, 双通道特征融合网络

Abstract:

The sea surface corner reflector exhibits extremely strong radar echo characteristics. It creates the false targets that interferes continuously in the time domain and generates a deceptive situation to bring a significant challenge to the precision strike capability of a seeker. To address this issue, the immunity of infrared sensors to corner reflector interference is leveraged, and an intelligent recognition algorithm for corner reflectors in multi-target scenarios based on radar-infrared feature-level fusion is proposed. The target interference in infrared images is preliminarily discriminated by YOLOv8 network. The high-confidence target images can directly output the recognition results. The low-confidence target images are individually cropped for target correlation using radar-infrared angular information and radar feature extraction. The radar features and infrared images are input into a dual-channel fusion network, achieving the secondary recognition of low-confidence targets. The measured data validate that the recognition accuracy of the proposed method exceeds 96%. The research work has significant reference value for corner reflector interference recognition.

Key words: corner reflector, target recognition, radar-infrared composite guidance, dual-channel feature fusion network

中图分类号:

TN974

孙殿星, 窦钥聪, 彭锐晖, 董云龙, 郭玮. 基于雷达-红外成像特征级融合的角反射器智能识别算法[J]. 兵工学报, 2025, 46(5): 240501-.

SUN Dianxing, DOU Yuecong, PENG Ruihui, DONG Yunlong, GUO Wei. An Intelligent Corner Reflector Recognition Algorithm Based on Radar-infrared Imaging Feature-level Fusion[J]. Acta Armamentarii, 2025, 46(5): 240501-.

图/表 15

图1 红外图像数据集

Fig.1 Infrared image dataset

图2 目标与干扰时频分布

Fig.2 Time-frequency distributions of target and interference

图3 算法流程图

Fig.3 Algorithm flowchart

图4 YOLOv8-P2网络结构

Fig.4 Network structure of YOLOv8-P2

图5 精度-置信度曲线图

Fig.5 Accuracy-confidence curve

图6 多模态特征融合网络结构图

Fig.6 Structure diagram of multi-mode feature fusion network

图7 算法总体流程图

Fig.7 Overall flowchar of the algorithm

图8 初判别部分数据(上为未识别图像,下为识别图像)

Fig.8 Partial data of initial discrimination (upper: the unrecognized images, below: the recognized images)

图9 关联准确率

Fig.9 Correlation accuracy

图10 融合网络二次判别部分数据(上为红外剪裁数据,下为雷达微动数据)

Fig.10 Partial data of fusion network secondary discrimination (upper: the infrared clipping data, below: the radar micro-motion data)

表1 融合方法在不同条件下识别结果

Table 1 Identification results of fusion method under different conditions

实验	先验信息	准确率/%
大船与角反射器	已知	99.85
小船与角反射器	已知	98.71
云雾遮挡下船舶与角反射器	未知	96.46

图11 训练过程损失变化曲线

Fig.11 Loss curve of training process

表2 对比实验1

Table 2 Comparative experiment 1

实验1	数据	准确率/%
本文实验	红外+雷达	98.71
单模雷达	雷达	85.35
单模红外	红外	88.07

表3 对比实验2

Table 3 Comparative experiment 2

实验2	数据	准确率/%
本文实验	红外+雷达	96.46
单模雷达	雷达	86.85
单模红外	红外	79.08

表4 文献[7]对比实验结果

Table 4 Comparison of experimental results in Ref. [7]

目标类型	文献[7]方法	本文方法
船舶	71.08	98.25
角反射器	74.76	98.43

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