海上微弱目标雷达-红外多视角协同智能TBD技术

孙殿星; 黄亚圣; 彭锐晖; 谭顺成; 王国宏

doi:10.12263/DZXB.20250887

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海上微弱目标雷达-红外多视角协同智能TBD技术

学术论文 | 更新时间：2026-04-24

- 海上微弱目标雷达-红外多视角协同智能TBD技术
- Radar-Infrared Multi-View Cooperative Intelligent TBD Technology for Maritime Weak Target Detection
- 电子学报 2025年53卷第12期页码：4686-4707
- 作者机构：
  
  1.哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150000
  2.青岛哈尔滨工程大学创新发展中心，山东青岛 266000
  3.海军航空大学信息融合研究所，山东烟台 264000
- 作者简介：
  
  [ "孙殿星男，1983年6月出生于黑龙江省伊春市.现为哈尔滨工程大学信息与通信工程学院教授.主要研究方向为雷达信号处理、雷达数据处理以及雷达抗干扰.E-mail: sundianxing@hrbeu.edu.cn" ]
  [ "黄亚圣男，2001年12月出生于江西省赣州市.现为哈尔滨工程大学信息与通信工程学院博士研究生.主要研究方向为雷达信号处理、多源信息融合以及智能检测.E-mail: huangyasheng@hrbeu.edu.cn" ]
  [ "彭锐晖男，1979年9月出生于湖北省武汉市.现为哈尔滨工程大学信息与通信工程学院教授.主要研究方向为雷达信号处理、雷达目标特性以及雷达抗干扰.中国电子学会会员编号：E190183282M.E-mail: pengruihui@hrbeu.edu.cn" ]
  [ "谭顺成男，1985年12月出生于湖南省湘潭市.现为海军航空大学副教授.主要研究方向为雷达信号处理、雷达数据处理、雷达抗干扰以及微弱目标检测.E-mail: tanshuncheng85@sina.com" ]
  [ "王国宏男，1963年10月出生于山西省沁水县.现为海军航空大学教授、博士生导师.主要研究方向为多源信息融合、雷达数据处理、雷达抗干扰以及微弱目标检测.E-mail: wangguohong@vip.sina.com" ]
- 基金信息：
  
  泰山学者项目(tstp20250747)
- DOI：10.12263/DZXB.20250887
  中图分类号： TN953;TN219
- 收稿：2025-11-21，
  
  录用：2025-12-11，
  
  纸质出版：2025-12-25
- 稿件说明：
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孙殿星, 黄亚圣, 彭锐晖, 等. 海上微弱目标雷达-红外多视角协同智能TBD技术[J]. 电子学报, 2025, 53(12): 4686-4707.

SUN Dian-xing, HUANG Ya-sheng, PENG Rui-hui, et al. Radar-Infrared Multi-View Cooperative Intelligent TBD Technology for Maritime Weak Target Detection[J]. Acta Electronica Sinica, 2025, 53(12): 4686-4707.
孙殿星, 黄亚圣, 彭锐晖, 等. 海上微弱目标雷达-红外多视角协同智能TBD技术[J]. 电子学报, 2025, 53(12): 4686-4707. DOI：10.12263/DZXB.20250887

SUN Dian-xing, HUANG Ya-sheng, PENG Rui-hui, et al. Radar-Infrared Multi-View Cooperative Intelligent TBD Technology for Maritime Weak Target Detection[J]. Acta Electronica Sinica, 2025, 53(12): 4686-4707. DOI：10.12263/DZXB.20250887

摘要

海上微弱目标检测通常面临目标雷达散射截面积小、红外对比度低、易受海杂波、漂浮物、岛礁及海鸟等背景干扰影响的难题，单一传感器检测方法受自身物理特性限制，在复杂海况下难以兼顾检测概率与虚警抑制性能.检测前跟踪（Track-Before-Detect，TBD）技术通过多帧联合处理能够有效提升微弱目标检测能力，但传统TBD方法多依赖先验运动模型，且主要针对单一传感器场景，在目标机动或背景复杂条件下适应性不足.针对上述问题，本文提出一种雷达-红外多视角协同智能TBD技术，实现复杂海面环境下微弱目标的高可靠检测.首先，针对雷达回波中背景杂波和噪声点迹密集的问题，引入雷达低门限预处理机制，在保留目标回波信息的同时剔除部分低幅值干扰点迹，降低后续处理的计算复杂度.随后，针对雷达与红外传感器在量测维度和空间表达上的异构性，构建雷达-红外异构数据空间映射模型，将雷达距离-方位量测映射至红外像平面，生成雷达-红外虚拟融合图像，实现两类传感器信息在统一像素空间内的对齐与融合，从而提升目标量测数据率并增强目标显著性.在融合图像构建基础上，采用最大值多帧累积策略对连续融合图像进行能量叠加，突出微弱目标的空时相关性并抑制随机噪声.同时，利用真实目标在雷达与红外两种传感器中均产生响应，而部分背景干扰仅在单一传感器中出现的跨模态差异特征，构建基于雷达-红外响应联合约束的目标潜在区域划定机制，有效排除海鸟、浮漂等非目标干扰，为后续检测提供可靠的空间约束，从而显著降低虚警率.在目标检测阶段，针对多帧累积融合图像中微弱目标尺度小、对比度低、航迹呈细长连续分布的特点，本文基于YOLOv11框架构建自适应多尺度特征增强网络（Adaptive Multi-Scale Feature Enhancement YOLOv11，AMSFE-YOLOv11），实现目标航迹检测与实例分割，并进一步抑制复杂背景干扰.该方法摆脱了传统TBD对先验运动模型的依赖，在目标发生机动的情况下仍能够有效提取航迹特征和空时相关性，实现稳定的能量累积，具有良好的鲁棒性和适用性.最后，基于海上实测雷达与红外数据对所提出方法进行了验证.结果表明，该方法对于微弱目标检测概率达到94.7%以上，虚警率为0.52%以下，相较于单一传感器检测方法表现出明显优势，验证了所提雷达-红外多视角协同智能TBD技术在复杂海面环境下的有效性和工程应用潜力.

Abstract

Weak maritime target detection usually faces challenges such as small radar cross sections

low infrared contrast

and susceptibility to background interference from sea clutter

floating objects

islands

and seabirds. Due to inherent physical limitations

single-sensor detection methods have difficulty balancing detection probability and false-alarm suppression under complex sea conditions. Track-before-detect (TBD) techniques can effectively enhance weak target detection through multi-frame joint processing; however

traditional TBD methods mostly rely on prior motion model assumptions and are primarily designed for single-sensor scenarios

showing insufficient adaptability under target maneuvering or complex background conditions. To address these issues

this paper proposes a radar-infrared multi-view cooperative intelligent TBD technique to achieve reliable detection of weak targets in complex maritime environments. First

to deal with the dense background clutter and noise points in radar echoes

a radar low-threshold preprocessing mechanism is introduced

which removes part of the low-amplitude interference points while preserving target echo information

thereby reducing the computational complexity of subsequent processing. Then

considering the heterogeneity of radar and infrared sensors in measurement dimensions and spatial representations

a radar-infrared heterogeneous data spatial mapping model is constructed. Radar range-azimuth measurements are mapped onto the infrared image plane to generate radar-infrared virtual fusion images

enabling the alignment and fusion of the two types of sensor information in a unified pixel space

thus increasing the target measurement data rate and enhancing target saliency. Based on the constructed fusion images

a maximum-value multi-frame accumulation strategy is employed to perform energy accumulation on consecutive fusion images

highlighting the spatio-temporal correlation of weak targets and suppressing random noise. Meanwhile

by exploiting the cross-modal difference that real targets generate responses in both radar and infrared sensors

whereas some background interference appears only in a single sensor

a candidate target region delineation mechanism based on joint radar-infrared response constraints is established. This mechanism effectively excludes non-target interference such as seabirds and floating objects

provides reliable spatial constraints for subsequent detection

and significantly reduces false-alarm rates. In the target detection stage

considering that weak targets in multi-frame accumulated fusion images are characterized by small size

low contrast

and elongated continuous trajectories

an adaptive multi-scale feature enhancement network based on the YOLOv11 framework (AMSFE-YOLOv11) is constructed to achieve target trajectory detection and instance segmentation

while further suppressing complex background interference. The proposed method eliminates the dependence of traditional TBD approaches on prior motion models and can still effectively extract trajectory features and spatio-temporal correlations under target maneuvering conditions

achieving stable energy accumulation with good robustness and applicability. Finally

the proposed method is validated using real maritime radar and infrared data. The experimental results demonstrate that the proposed method achieves a detection probability exceeding 94.7% for weak targets

with a false alarm rate below 0.52%. Compared with single-sensor detection approaches

it exhibits a clear performance advantage

thereby validating the effectiveness and practical application potential of the proposed radar-infrared multi-view cooperative intelligent TBD technique in complex maritime environments.

关键词

Keywords

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