An Intelligent Corner Reflector Recognition Algorithm Based on Radar-infrared Imaging Feature-level Fusion

doi:10.12382/bgxb.2024.0501

Abstract

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

CLC Number:

TN974

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-.

Figures/Tables 15

Fig.1 Infrared image dataset

Fig.2 Time-frequency distributions of target and interference

Fig.3 Algorithm flowchart

Fig.4 Network structure of YOLOv8-P2

Fig.5 Accuracy-confidence curve

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

Fig.7 Overall flowchar of the algorithm

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

Fig.9 Correlation accuracy

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

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

Fig.11 Loss curve of training process

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

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

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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