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Highly maneuvering target tracking using multi-parameter fusion Singer model 被引量:8
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作者 Shuyi Jia Yun Zhang Guohong Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第5期841-850,共10页
An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin... An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples. 展开更多
关键词 maneuvering target multi-parameter fusion Singer (MF-Singer) fuzzy reasoning Singer model
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Fusion network for small target detection based on YOLO and attention mechanism 被引量:5
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作者 XU Caie DONG Zhe +3 位作者 ZHONG Shengyun CHEN Yijiang PAN Sishun WU Mingyang 《Optoelectronics Letters》 EI 2024年第6期372-378,共7页
Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of r... Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better. 展开更多
关键词 fusion network for small target detection based on YOLO and attention mechanism
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Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm 被引量:7
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作者 Chuang Lin Fei Peng +2 位作者 Bing-Hui Wang Wei-Feng Sun Xiang-Jie Kong 《Journal of Electronic Science and Technology》 CAS 2012年第4期352-357,共6页
This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis (PCA) and nonlinear featu... This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis (PCA) and nonlinear feature extracted from kernel principal component analysis (KPCA) respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion (WMMC) to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms. 展开更多
关键词 Automatic target recognition principal component analysis self-fusion syntheticaperture radar.
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Formation Process of Magnetized Fusion Target on the YingGuang 1 Device
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作者 李璐璐 贾月松 +6 位作者 孙奇志 刘伟 刘正芬 秦卫东 李军 池原 杨显俊 《Chinese Physics Letters》 SCIE CAS CSCD 2016年第4期80-83,共4页
Magnetized target fusion is an alternative method to fulfill the goal of controlled fusion, which combines advan- tages of both magnetic confinement fusion and inertial confinement fusion since its parameter space lie... Magnetized target fusion is an alternative method to fulfill the goal of controlled fusion, which combines advan- tages of both magnetic confinement fusion and inertial confinement fusion since its parameter space lies between the two traditional ways. Field reversed configuration (FFtC) is a good candidate of magnetized targets due to its translatable, compressible, high /3 and high energy density properties. Dynamic formation process of high density FFtC is observed on the YingGuang 1 device for the first time in China. The evolution of a magnetic field is detected with magnetic probes, and the compression process can be clearly seen from images taken with a high-speed multi-frame CCD camera. The process is also studied with two-dimensional magneto hydrodynamic code MPF-2D theoretically, and the results agree well with the experiment. Combining the experimental data and the theoretical analysis, the length of the formed FRC is about 39 cm, the diameter is about 2-2. 7cm, the average density is 1.3× 1016 cm-3, and the average temperature is 137eV. 展开更多
关键词 of Formation Process of Magnetized fusion target on the YingGuang 1 Device is for FRC in ICF high with on
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A High-Resolution Measurement Method for Inner and Outer 3D Surface Profiles of Laser Fusion Targets Using a Laser Differential Confocal–Atomic Force Probe Technique
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作者 Weiqian Zhao Zihao Liu Lirong Qiu 《Engineering》 SCIE EI CAS CSCD 2024年第10期51-60,共10页
The high-resolution and nondestructive co-reference measurement of the inner and outer threedimensional(3D)surface profiles of laser fusion targets is difficult to achieve.In this study,we propose a laser differential... The high-resolution and nondestructive co-reference measurement of the inner and outer threedimensional(3D)surface profiles of laser fusion targets is difficult to achieve.In this study,we propose a laser differential confocal(LDC)–atomic force probe(AFP)method to measure the inner and outer 3D surface profiles of laser fusion targets at a high resolution.This method utilizes the LDC method to detect the deflection of the AFP and exploits the high spatial resolution of the AFP to enhance the spatial resolution of the outer profile measurement.Nondestructive and co-reference measurements of the inner profile of a target were achieved using the tomographic characteristics of the LDC method.Furthermore,by combining multiple repositionings of the target using a horizontal slewing shaft,the inner and outer 3D surface profiles of the target were obtained,along with a power spectrum assessment of the entire surface.The experimental results revealed that the respective axial and lateral resolutions of the outer profile measurement were 0.5 and 1.3 nm,while the respective axial and lateral resolutions of the inner profile measurement were 2.0 nm and approximately 400.0 nm.The repeatabilities of the rootmean-square deviation measurements for the outer and inner profiles of the target were 2.6 and 2.4 nm,respectively.We believe our study provides a promising method for the high-resolution and nondestructive co-reference measurement of the inner and outer 3D profiles of laser fusion targets. 展开更多
关键词 Laser fusion targets Laser differential confocal-atomic force probe HIGH-RESOLUTION Nondestructive Co-reference
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SMALL TARGET TRACKING TECHNIQUE WITH DATA FUSION OF DISTRIBUTED SENSOR NET
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作者 程洪玮 周一宇 孙仲康 《Chinese Journal of Aeronautics》 SCIE EI CSCD 1998年第2期29-36,共8页
SMALLTARGETTRACKINGTECHNIQUEWITHDATAFUSIONOFDISTRIBUTEDSENSORNETCHENGHongwei(程洪玮),ZHOUYiyu(周一宇),SUNZhongkang... SMALLTARGETTRACKINGTECHNIQUEWITHDATAFUSIONOFDISTRIBUTEDSENSORNETCHENGHongwei(程洪玮),ZHOUYiyu(周一宇),SUNZhongkang(孙仲康)(Faculty406,... 展开更多
关键词 small target tracking date fusion distributed sensor net
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Algorithm for Multi-laser-target Tracking Based on Clustering Fusion
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作者 张立群 李言俊 张科 《Defence Technology(防务技术)》 SCIE EI CAS 2007年第1期28-32,共5页
Multi-laser-target tracking is an important subject in the field of signal processing of laser warners. A clustering method is applied to the measurement of laser warner, and the space-time fusion for measurements in ... Multi-laser-target tracking is an important subject in the field of signal processing of laser warners. A clustering method is applied to the measurement of laser warner, and the space-time fusion for measurements in the same cluster is accomplished. Real-time tracking of multi-laser-target and real-time picking of multi-laser-signal are introduced using data fusion of the measurements. A prototype device of the algorithm is built up. The results of experiments show that the algorithm is very effective. 展开更多
关键词 激光报警器 多目标跟踪 算法 聚类融合 信息处理
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Three dimensional passive underwater target motion analysis using correlated data fusion
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作者 HU Youfeng, JIAO Bingli (Department of Electrics, Peking University, Beijing 100871, China) 《声学技术》 CSCD 2004年第S1期43-48,共6页
In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that cons... In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship. 展开更多
关键词 PASSIVE localization target motion analysis (TMA) data fusion
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ATMS Based Information Fusion Target Recognition Method
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作者 陈文颉 窦丽华 张宇河 《Journal of Beijing Institute of Technology》 EI CAS 2003年第S1期12-15,共4页
The non-monotonic problem exited in information fusion systems is solved. Through the introducing of non-monotonic reasoning method, which was realized with ATMS, into the information fusion system, it gains the abili... The non-monotonic problem exited in information fusion systems is solved. Through the introducing of non-monotonic reasoning method, which was realized with ATMS, into the information fusion system, it gains the ability to process insufficient information with flexibility and non-monotonic behavior. In the simulation test of our system, our system manifests its ability of dealing the insufficient and contradictory information, which partly solves the decision dilemma brought out by the insufficient information in battle situations. The information fusion target recognition system can process the information in battle situation fast and with flexibility. 展开更多
关键词 non-monotonic reasoning data fusion TMS ATMS: target recognition
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Fusion Reaction Rate Coefficient for Different Beam and Target Scenarios
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作者 欧巍 曾宪俊 +1 位作者 邓柏权 苟富均 《Chinese Physics Letters》 SCIE CAS CSCD 2015年第2期43-47,共5页
Fusion power output is proportional not only to the fuel particle number densities participating in reaction but also to the fusion reaction rate coefficient (or reactivity), which is dependent on reactant velocity ... Fusion power output is proportional not only to the fuel particle number densities participating in reaction but also to the fusion reaction rate coefficient (or reactivity), which is dependent on reactant velocity distribution functions. They are usuMly assumed to be dual Maxwellian distribution functions with the same temperature for thermal nuclear fusion circumstances. However, if high power neutral beam injection and minority ion species ICRF plasma heating, or multi-pinched plasma beam head-on collision, in a converging region are required and investigated in future large scale fusion reactors, then the fractions of the injected energetic fast ion tail resulting from ionization or charge exchange will be large enough and their contribution to the non-Maxwellian distribution functions is not negligible, hence to the fusion reaction rate coefficient or calculation of fusion power. In such cases, beam-target, and beam-beam reaction enhancement effect contributions should play very important roles. In this paper, several useful formulae to calculate the fusion reaction rate coefticient for different beam and target combination scenarios are derived in detail 展开更多
关键词 fusion Reaction Rate Coefficient for Different Beam and target Scenarios exp
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基于YOLO-BioFusion的血细胞检测模型 被引量:1
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作者 张傲 刘微 +2 位作者 刘阳 杨思瑶 管勇 《电子测量技术》 北大核心 2025年第18期177-188,共12页
血细胞检测是临床诊断中的重要任务,尤其在面对细胞类型多样、尺寸差异显著、目标重叠频繁以及复杂背景时,现有检测模型的精度和鲁棒性仍面临挑战。为解决这些问题,本文提出了一种改进的YOLOv8目标检测模型——YOLO-BioFusion。该模型... 血细胞检测是临床诊断中的重要任务,尤其在面对细胞类型多样、尺寸差异显著、目标重叠频繁以及复杂背景时,现有检测模型的精度和鲁棒性仍面临挑战。为解决这些问题,本文提出了一种改进的YOLOv8目标检测模型——YOLO-BioFusion。该模型通过引入ACFN模块,提高了对细小目标和重叠目标的检测能力;应用C2f-DPE和SPPF-LSK模块增强了多尺度特征的融合与提取,提升了模型的鲁棒性和泛化能力;同时,采用Inner-CIoU损失函数加速了模型收敛并提高了定位精度。实验结果表明,在BCCD数据集上,YOLO-BioFusion的mAP@0.5为94.0%,mAP@0.5:0.95为65.2%,分别较YOLOv8-n提高了1.9%和3.2%。与此同时,计算成本仅为6.8 GFLOPs,展示了其在资源受限环境中的应用潜力。该研究为复杂背景下的血细胞检测提供了一种高效且精确的解决方案。 展开更多
关键词 血细胞检测 多尺度特征融合 损失函数优化 YOLOv8 重叠目标
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改进的有雾图像中被遮挡车辆及行人识别算法
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作者 于天河 王文龙 +2 位作者 刘镛 杨壮壮 侯善冲 《浙江大学学报(工学版)》 北大核心 2026年第4期738-750,共13页
针对智能自动驾驶场景中目标遮挡与雾天干扰导致的目标检测精度下降问题,提出2项关键技术改进.针对目标遮挡问题提出改进检测方法,以集成增强注意力机制的轻量化MobileNetV3_small作为SSD骨干特征提取网络,结合多尺度特征融合机制与自... 针对智能自动驾驶场景中目标遮挡与雾天干扰导致的目标检测精度下降问题,提出2项关键技术改进.针对目标遮挡问题提出改进检测方法,以集成增强注意力机制的轻量化MobileNetV3_small作为SSD骨干特征提取网络,结合多尺度特征融合机制与自适应超参数Soft-NMS算法提升遮挡场景下的检测精度,通过改进自适应Focal Loss重构置信度损失函数,缓解正负样本不平衡及噪声标签敏感性问题.针对雾天图像中目标模糊的问题提出改进轻量化AOD-Net去雾方法,通过构建基于深度可分离卷积的多尺度特征提取网络,优化跨层连接结构并引入边界增强模块,有效提升图像对比度、增强纹理细节.通过联合损失函数对去雾网络与检测网络进行端到端协同优化,为有雾图像中的遮挡目标检测任务提供更可靠的优化路径.实验结果表明,联合优化模型提升了雾天遮挡场景下的目标检测性能,以93.85%的准确率和47.61帧/s的检测速度实现了高效检测,并表现出优异的模型鲁棒性. 展开更多
关键词 被遮挡目标 雾天图像 特征融合 目标检测 特征提取
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基于TUFusion的无人机可见光与红外融合检测算法研究
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作者 刘宏宇 《软件》 2025年第8期39-43,共5页
针对无人机复杂环境下目标检测的鲁棒性问题,本文提出了一种基于TUFusion的多模态融合检测算法。通过结合TUFusion网络的混合编码器与复合注意力机制,实现了可见光与红外图像的特征级深度融合;引入Dempster-Shafer(DS)证据理论,对多源... 针对无人机复杂环境下目标检测的鲁棒性问题,本文提出了一种基于TUFusion的多模态融合检测算法。通过结合TUFusion网络的混合编码器与复合注意力机制,实现了可见光与红外图像的特征级深度融合;引入Dempster-Shafer(DS)证据理论,对多源检测结果进行决策级融合,有效降低了误检与漏检。在DroneVehicle数据集上的实验表明,算法在mAP@0.5、精确率与召回率上分别达到0.905、92.1%和89.7%,显著优于传统像素级融合与单一模态检测。该方法通过多层次信息融合,为无人机全天候目标检测提供了高精度、高鲁棒性的解决方案。 展开更多
关键词 多模态融合 无人机目标检测 TUfusion网络 红外与可见光图像 决策级融合
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基于域对抗的自适应环境运动目标状态检测方法
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作者 肖刚 吴沛熙 +4 位作者 丁浩南 陈锋 徐雪松 袁牧 程振波 《高技术通讯》 北大核心 2026年第1期67-79,共13页
生物式水质预警系统常将鱼类作为生物指示器,通过自动获取监测箱内鱼目标的行为状态,实现对流经监测箱水体的源水水质预警。然而,在源水杂质、藻类和水垢等长时间的影响下,使用现有的深度学习框架检测监测箱内的鱼目标,其准确性会逐渐... 生物式水质预警系统常将鱼类作为生物指示器,通过自动获取监测箱内鱼目标的行为状态,实现对流经监测箱水体的源水水质预警。然而,在源水杂质、藻类和水垢等长时间的影响下,使用现有的深度学习框架检测监测箱内的鱼目标,其准确性会逐渐下降。为此,本文提出了一种自适应环境鱼目标检测模型,该模型包括前景融合处理模块、域对抗模块以及目标检测模块。前景融合模块将输入图像与其包含的目标轮廓二值图像融合作为模型的输入,域对抗模块中的域分类网络经由梯度反转层来实现对输入图像所处域的分类,目标检测模块通过弱监督来增加不同域下训练数据的标注信息。最后,通过改变监测箱背景构建了5类不同水体环境的数据集并在这些数据集上进行实验,结果表明模型在监测箱水体环境发生变化的情况下,依然能较准确地对鱼目标进行状态分类。 展开更多
关键词 生物水质监测 域适应 目标检测 前景融合
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基于改进RT-DETR的遥感图像目标检测算法
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作者 肖锋 杨文豪 +2 位作者 张文娟 黄姝娟 周雨洁 《电子测量技术》 北大核心 2026年第2期192-202,共11页
遥感图像中的目标常呈细长、曲折等复杂形态,且伴随尺度变化大与背景干扰强等因素,导致现有检测方法易出现缺检和误检,难以满足高精度检测需求,为此,提出一种改进的遥感图像目标检测算法TriD-DETR。首先,通过动态调整卷积核形状并优化... 遥感图像中的目标常呈细长、曲折等复杂形态,且伴随尺度变化大与背景干扰强等因素,导致现有检测方法易出现缺检和误检,难以满足高精度检测需求,为此,提出一种改进的遥感图像目标检测算法TriD-DETR。首先,通过动态调整卷积核形状并优化通道适配与残差连接方式,设计了DKFE特征提取模块,该模块能够自适应地聚焦于细长曲折的局部区域,从而准确捕捉目标特征;其次,为了提高模型对复杂目标的定位和识别能力,提出DATE尺度内特征交互结构,在重构Transformer编码器的基础上引入可变形注意力机制,增强了模型对高级特征和深层语义信息的捕捉能力;最后,针对多尺度特征融合部分,提出DBFB多样性分支融合模块,通过组合不同尺度和复杂度的多样性分支使特征空间更丰富,从而增强模型的表达能力。实验结果表明,TriD-DETR算法在DIOR和RSOD数据集上分别达到86.8%和94.1%的mAP,相较于原模型RT-DETR-R18,分别提升了1.2%和2.3%,充分证明了TriD-DETR算法的可靠性与高效性。 展开更多
关键词 遥感图像 目标检测 RT-DETR 注意力机制 多尺度特征融合
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无人机目标跟踪的形变感知与特征融合研究
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作者 罗旭浩 陈文玉 +2 位作者 张桂龙 房建 赵怀慈 《计算机工程与应用》 北大核心 2026年第1期307-316,共10页
虽然基于相关滤波的跟踪算法凭借着其出色性能和较低的计算复杂度在无人机平台得到了广泛应用,但其对目标形变感知和异常检测的方法较为单一,因此难以应对现实场景下的复杂跟踪任务。为此提出一种基于目标形变感知与特征融合的无人机目... 虽然基于相关滤波的跟踪算法凭借着其出色性能和较低的计算复杂度在无人机平台得到了广泛应用,但其对目标形变感知和异常检测的方法较为单一,因此难以应对现实场景下的复杂跟踪任务。为此提出一种基于目标形变感知与特征融合的无人机目标跟踪方法——DPFF。该方法通过差分百分比的形式构建形变感知张量;并根据形变感知张量在空间上的分布构建空域掩膜与时域惩罚系数,实现自适应时空正则;同时根据形变感知张量和响应图分布构造基于特征融合的异常检测网络,通过深入挖掘二者的分布畸变信息防止在跟踪状态异常时模型受到背景噪声的污染。在VisDrone和OTB100等公开数据集上的测试结果表明,与目前先进的AutoTrack算法及其他主流跟踪算法相比,所提出算法在目标跟踪的准确率和成功率上均有提升,具有更强的鲁棒性能。 展开更多
关键词 相关滤波 形变感知 特征融合 目标跟踪
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基于多特征融合网络的空袭目标运动状态辨识
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作者 梁桐嘉 郑欣磊 +1 位作者 刘雅梁 范继 《兵工学报》 北大核心 2026年第2期241-257,共17页
针对防空火力控制系统对空袭目标运动状态辨识能力欠缺所导致的轨迹预测精度受限及毁伤效能低下问题,提出一种面向火控系统的分类范式以及基于多特征融合神经网络(Multi-feature Fusion Neural Network, MFF-Net)的运动状态智能辨识方... 针对防空火力控制系统对空袭目标运动状态辨识能力欠缺所导致的轨迹预测精度受限及毁伤效能低下问题,提出一种面向火控系统的分类范式以及基于多特征融合神经网络(Multi-feature Fusion Neural Network, MFF-Net)的运动状态智能辨识方法。通过构建三维近似熵特征描述子,系统量化空袭目标轨迹的时空复杂度特性;提出卷积注意力耦合机制,实现轨迹复杂度特征与卷积长短期记忆网络提取的时空关联特征之间的自适应融合;引入一维卷积模块强化时序动态特征的层次化提取,与注意力得分进行二次融合加强模型辨识能力。实验结果表明:基于雷达实测数据与典型运动模式仿真数据集构建的混合测试集上MFF-Net在稳态线性、盘旋等四类典型运动范式的辨识准确率达到96.56%,较长短期记忆网络或一维卷积网络等时序网络相比有着显著提升,验证了该方法对复杂轨迹运动状态辨识的有效性,为复杂战场环境下空袭目标运动模式在线辨识提供了融合特征量化与深度学习的复合框架。 展开更多
关键词 神经网络 运动状态辨识 空袭目标 特征融合 火力控制系统
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知识-数据-模型驱动的低空动目标轨迹融合预测方法
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作者 周同乐 刘子仪 陈谋 《自动化学报》 北大核心 2026年第2期296-308,共13页
针对低空环境下动目标轨迹预测问题,提出一种知识-数据-模型驱动的动目标轨迹融合预测框架.基于低空飞行器运动特征构建飞行知识混合专家模型,通过将多源传感器数据输入至各飞行知识专家模块,实现目标机动模态的精细化识别,并使用Mamba... 针对低空环境下动目标轨迹预测问题,提出一种知识-数据-模型驱动的动目标轨迹融合预测框架.基于低空飞行器运动特征构建飞行知识混合专家模型,通过将多源传感器数据输入至各飞行知识专家模块,实现目标机动模态的精细化识别,并使用Mamba模型提取时空关联特征;设计权值自适应调节机制,利用注意力机制动态融合多源感知数据,解决传感器时空异步问题;采用门控循环单元建模长期时序依赖关系,根据目标历史飞行数据生成初步预测轨迹;基于低空目标运动学方程构建物理信息神经网络,通过动态权衡数据驱动损失与物理约束损失,矫正数据驱动偏差,确保预测轨迹满足运动学约束并有效抑制多步预测误差累积.数值仿真及实验验证结果表明,所提出的知识-数据-模型驱动的动目标轨迹融合预测方法,能够有效预测低空目标飞行轨迹. 展开更多
关键词 低空环境 知识-数据-模型驱动 动目标 数据融合 轨迹预测
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改进YOLOv11的无人机航拍小目标检测算法
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作者 景婷婷 曹玉东 +1 位作者 陈鑫 王冬霞 《计算机工程与应用》 北大核心 2026年第2期138-148,共11页
无人机航拍图像中小目标的占比高,机载平台资源有限,导致现有的检测模型性能较弱,难以在检测性能和资源消耗之间取得良好的平衡。为了缓解上述问题,提出一种改进的YOLOv11模型。构建MDPE模块作为主干网络的注意力模块,通过引入动态通道... 无人机航拍图像中小目标的占比高,机载平台资源有限,导致现有的检测模型性能较弱,难以在检测性能和资源消耗之间取得良好的平衡。为了缓解上述问题,提出一种改进的YOLOv11模型。构建MDPE模块作为主干网络的注意力模块,通过引入动态通道分割策略和轻量级平滑网络,实现特征资源自适应分配与局部细节增强;设计DAFI模块替换颈部网络中的特征融合模块,通过特征增强模块结合双动态加权机制,增强算法对目标空间分布的感知能力;在检测头的回归分支中嵌入区块化动态对齐卷积,实现特征图的自适应对齐和融合,提高模型对不同尺度目标的检测能力;引入长宽比差异项,并结合小目标权重调整机制,设计ARSIoU损失函数,提升模型对小目标的定位精度与边界框回归效率。在VisDrone2019数据集上的实验表明,改进后的模型相较于原始YOLOv11模型,在精确率、召回率、mAP@50性能指标上分别提升3.5、1.9、3.2个百分点,适用于复杂场景下的无人机航拍小目标检测应用。 展开更多
关键词 小目标检测 多尺度特征融合 多维度注意力
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双域特征融合和重校准小目标检测网络
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作者 卢宇东 李华 +2 位作者 任德均 程科然 张智勇 《计算机与现代化》 2026年第2期39-45,52,共8页
针对目标检测领域中小目标检测区域小、特征像素少、识别效果差等问题,提出一种基于RT-DETR改进的小目标检测算法DFFR-DETR。首先,提出空间频率双域特征融合模块来改进主干网络,命名为SDFF-Net,替换原主干网络,提取图像中小目标的结构特... 针对目标检测领域中小目标检测区域小、特征像素少、识别效果差等问题,提出一种基于RT-DETR改进的小目标检测算法DFFR-DETR。首先,提出空间频率双域特征融合模块来改进主干网络,命名为SDFF-Net,替换原主干网络,提取图像中小目标的结构特征,增强对小目标信息的捕捉能力。其次,引入重校准注意单元和RepConv设计边界聚合重参数化(BAR)模块,提升网络对多尺度特征的融合与提取能力。最后,基于BAR模块设计基于卷积神经网络的多尺度特征重校准模型,增强全局特征的提取能力,进一步改善小目标检测性能。在VisDrone2019数据集上的实验结果表明,DFFR-DETR模型在验证集和测试集上的mAP_(50)分别达到了52.6%和41.3%,比基线模型提高了5.1百分点和4.0百分点,此外精确率、召回率也有不同程度的提升;在TinyPerson数据集上做了泛化性实验,DFFR-DETR模型相较于基线模型在召回率、mAP_(50)上分别提升了3.9百分点和2.3百分点,验证了改进模型的有效性和泛化性。 展开更多
关键词 小目标检测 RT-DETR 特征融合 特征金字塔
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