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IEEE National Conference on Radar

作者： Javier Trujillo Rodriguez Fabiola Colone Pierfrancesco Lombardo Dept. of Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

In this paper, we present an experimental evaluation of recently proposed Supervised Reciprocal Filter approaches for the compression of OFDM-radar signals. The range-Doppler map is usually evaluated using a suboptimal batches algorithm, after fragmenting the signal in batches with length equal to the OFDM symbol. Using “OFDM fragmentation” requires symbol synchronization and sets constraints on the non-ambiguous Range-Doppler area of targets that can be detected with limited signal-to-Noise Ratio (SNR) loss. Supervised Reciprocal Filters have been recently proposed to operate with batches of longer lengths than the OFDM symbol without requiring any synchronization. In this paper we extend the study to include the case of batches equal to a fraction of the OFDM symbol, which provides higher flexibility to adapt the processor to the range-Doppler scenario of interest. These filters have been shown to contain the large SNR losses obtained with a direct application of the Reciprocal Filter (RF) with the non-OFDM fragmentation. Moreover, they have been shown theoretically to preserve the benefits of the RF over the Matched Filter (MF) against the clutter-limited scenarios. To assess the performance of the Supervised Filter against a real scenario, an acquisition campaign has been carried out using the Sapienza experimental passive radar along the coast north of Rome, against a maritime traffic scenario, including non-cooperative vessels, as well as a cooperating small boat equipped with differential GPS positioning registration tools. The effectiveness of the proposed approaches is validated by applying them to experimental data from a PBR application exploiting DVB-T transmissions.

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气象雷达鸟群目标多维特征建模与特性分析

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信号处理 2025年第5期41卷 829-839页

作者：张梦晨赵志坚刘佳陈小龙苏添记房旭东薛伟关键哈尔滨工程大学烟台研究院山东烟台264000 海军航空大学山东烟台264001 北京航空航天大学电子信息工程学院北京100191 烟台市气象局山东烟台264003 92872部队辽宁葫芦岛125200

随着民航运输规模扩大及生态环境改善,鸟击事件频发,对航空安全构成威胁。飞鸟是典型的“低慢小”目标,具有低空飞行、飞行速度慢、体积小等特点,针对这种目标的识别和分类问题,其核心在于通过深度特征学习提取数据的显著特征,并结合目... 详细信息

随着民航运输规模扩大及生态环境改善,鸟击事件频发,对航空安全构成威胁。飞鸟是典型的“低慢小”目标,具有低空飞行、飞行速度慢、体积小等特点,针对这种目标的识别和分类问题,其核心在于通过深度特征学习提取数据的显著特征,并结合目标物理特性与多维特征模型进行综合分析。气象雷达适用于大尺度范围内的迁徙鸟群目标监测,并且易于有效组网,能够获得目标的多种特征信息。本文以航空安全背景下的鸟群及气象目标为例,基于气象雷达的实测数据,通过雷达多维特征的参数化表征,对鸟群目标、降水目标以及云目标进行特征分析,研究目标反射率、谱宽特征、差分相位特征等6种多维特征的差异性,通过直方图可视化分析、主成分分析及Fisher线性判别方法,揭示了反射率、相关系数等关键特征参数在区分鸟群目标与气象目标的显著统计差异,并验证了目标间的可分性。实验结果表明,鸟群目标的相关系数呈现单峰正态分布特征、谱宽分布相对分散、反射率高度聚集于10~20 dBZ区间、差分相位集中在75°~125°的较高值区间等特征,与气象类目标存在显著差异。通过对不同目标聚类并计算目标之间的二范数距离,进一步验证了鸟群类与气象目标呈现出显著的可分性特征,为鸟击预警系统的构建奠定了数据与理论基础。

关键词：气象雷达回波特征特性分析鸟击预警

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Using data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda 14

Using Data Analytics to Strengthen Monitoring and Surveillan...

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14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and signal processing (BIOsignalS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Nantongo, Bartha Alexandra Nabukenya, Josephine Nabende, Peter Makerere Univ Coll Comp & Informat Syst Dept Informat Syst POB 7062 Kampala Uganda

ISBN: (纸本)9789897584909

Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect of unstructured data and process indicators that track small changes/milestones. While empirical evidence indicates digitalized immunization systems establish coverage from structured data, renowned administrative and household survey estimates are often inaccurate/untimely. Government instituted awareness, accessibility, and results-based performance approaches, but stakeholders are challenged by accurate monitoring of performance against Global Vaccination Action Plan coverage targets. This heightens inappropriate strategy implementation leading to persistent low coverage and declining trends. There is scanty literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based and timely interventions. For this reason, health workers failed to appreciate immunization process indicators and monitoring role. The study aims at developing a real-time immunization coverage monitoring framework that supports evidence-based strategy implementation using prescriptive analytics. The envisaged artifact analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a less resource-demanding strategy that prompts accurate and real-time insights to support intervention implementation decisions. This study will follow an explanatory research approach by first collecting quantitative data and later qualitative for in-depth analysis.

关键词： data Analytics Monitoring Routine Immunization Coverage Performance

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Radar group target recognition based on HRRPs and weighted mean shift clustering

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Journal of Systems Engineering and Electronics 2020年第6期31卷 1152-1159页

作者： GUO Pengcheng LIU Zheng WANG Jingjing National Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China Xi'an Electronic Engineering Research Institute Xi’an 710100China

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the *** this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering *** the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier *** the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each ***,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be *** to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target *** experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

关键词： clustering group target recognition high resolution range profile(HRRP) mean shift(MS)

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Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar

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IEEE TRANSACTIONS ON signal processing 2020年第0期68卷 715-730页

作者： Kumari, Preeti Vorobyov, Sergiy A. Heath, Robert W., Jr. Univ Texas Austin Wireless Networking & Commun Grp Austin TX 78712 USA Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland

Joint communication and radar (JCR) waveforms with fully digital baseband generation and processing can now be realized at the millimeter-wave (mmWave) band. Prior work has developed a mmWave wireless local area network (WLAN)-based JCR that exploits the WLAN preamble for radars. The performance of target velocity estimation, however, was limited. In this paper, we propose a virtual waveform design for an adaptive mmWave JCR. The proposed system transmits a few non-uniformly placed preambles to construct several receive virtual preambles for enhancing velocity estimation accuracy, at the cost of only a small reduction in the communication data rate. We evaluate JCR performance trade-offs using the Cram& x00E9;r-Rao Bound (CRB) metric for radar estimation and a novel distortion minimum mean square error (MMSE) metric for data communication. Additionally, we develop three different MMSE-based optimization problems for the adaptive JCR waveform design. Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform. For a radar CRB constrained optimization, the optimal radar range of operation and the optimal communication distortion MMSE (DMMSE) are improved. For a communication DMMSE constrained optimization with a high DMMSE constraint, the optimal radar CRB is enhanced. For a weighted MMSE average optimization, the advantage of the virtual waveform over the uniform waveform is increased with decreased communication weighting. Comparison of MMSE-based optimization with traditional virtual preamble count-based optimization indicated that the conventional solution converges to the MMSE-based one only for a small number of targets and a high signal-to-noise ratio.

关键词： Millimeter wave radar millimeter wave communication joint communication-radar automotive applications adaptive waveform design velocity estimation

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Priority Scheduling Using Recurrent Quadrant Search for Handling Priority and ‘Pop-Up’ targets in Aerial Videos

Priority Scheduling Using Recurrent Quadrant Search for Hand...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Chandrakanth. V Shivpal Singh Sumohana S Channappayya K Palaniappan Defence Research and Development Laboratory Hyderabad Indian Institute of Technology Hyderabad University of Missouri Columbia

Target detection and tracking is a well established application in the radar (RF) domain. However, the same operating principles may not directly extend to image-based tracking systems. In the optical domain, the problem becomes challenging due to the lack of an active transmit signal (as available in radar processing), which actively maps the environment for target detection and track initiation. Passive imaging systems often require a separate assisted intervention in the optical processing channel for real-time target detection and track initialization. This limitation has been successfully addressed with the development of automated tracking algorithms using deep learning architectures like Convolutional Neural Networks (CNNs). CNNs are extensively employed in generic object classification and localization applications for single and multi-target scenarios. In multi-target scenarios, the deep architecture framework detects all the objects in the frame without any priority designation (eg. YOLO, VGG19 etc.). However, in multi-target scenarios where, handling selective targets is critical (eg. tracking an ambulance in traffic) or prioritized tracking is needed of a ‘Pop-Up’ target, that is an object entering the field-of-view (FOV)) (i.e. in autonomous driving systems, e-commerce deliveries etc.), the generic networks are not sufficient. In this work, we aim at addressing this specific problem of priority/ selective target tracking in aerial videos. We propose a novel CNN termed ‘PriorityNet’ with embedded priority information and a recurrent quadrant search algorithm for prioritized tracking and false alarm rejection in multi-target scenarios. The CNN and the associated algorithm is successfully deployed on a portable NVIDIA Jetson-Nano board (a small form factor GPU) integrated on-board a custom built UAV. The complete system is tested on multiple open-source and custom multi-modal data sets (RGB and Thermal). The results obtained demonstrate the efficacy of the

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Research on recognition algorithm of LSS based on video in airport clearance area 2

Research on recognition algorithm of LSS based on video in a...

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2nd IEEE International Conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： Qu, Jingyi Bi, Xinjie Liu, Shanliang Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin300300 China

ISBN: (纸本)9780738131221

LSS (Low, small and Slow) targets have characteristics of low cost, simple operation, easy to carry, low take-off requirements, strong sudden takeoff, and difficult to find and handle. With the development of aviation industry, the cases of LSS flying illegally and used in terrorist attacks are increasing day by day. It will not only cause great negative effects on economy, but also have serious influence on the national security and the normal development of the national economy. In order to eliminate the threat of illegal flight of UAV in the airport clearance area to the safe operation of airport, a LSS recognition algorithm based on the deep learning was proposed in the paper. Compared with other detection types, the video detection was used in the algorithm, which has many advantages such as strong visualization, low cost and fast detection speed. By using the Yolov3 object detection model, one of the most popular deep learning methods, the problem of low accuracy of small objects such as LSS can be solved. The experiments show that the Yolov3 model can improve the accuracy for small objects and can effectively detect the LSS. Thus, the necessary precondition for the subsequent LSS countermeasures can be provided, and the safe operation of the airport can be ensured. © 2021 IEEE.

关键词： Object detection

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一种机载组网雷达协同目标检测算法

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现代雷达 2024年第2期46卷 70-77页

作者：李洁玉丛潇雨郭山红盛卫星南京理工大学电子工程与光电技术学院江苏南京210094

多机协同的机载雷达组网联合目标探测可有效提高复杂电磁干扰环境下对隐身弱目标的探测能力。文中针对机载雷达组网探测时空间配准误差大、协同探测难以实现的难题,提出了一种基于轨迹空间配准的协同目标检测算法,通过雷达间少量距离-... 详细信息

多机协同的机载雷达组网联合目标探测可有效提高复杂电磁干扰环境下对隐身弱目标的探测能力。文中针对机载雷达组网探测时空间配准误差大、协同探测难以实现的难题,提出了一种基于轨迹空间配准的协同目标检测算法,通过雷达间少量距离-多普勒域数据及低检测门限下目标轨迹域数据的交互,采用极大似然估计广义似然比检测器对目标进行联合恒虚警检测(CFAR),并通过轨迹域空间配准与CFAR的迭代计算,实现配准精度和目标联合检测性能的双提升。数值仿真实验的结果表明,在四部雷达组网工作时,在相参积累后信噪比9 dB、虚警概率10-4的典型场景下,经过迭代处理,空间配准精度可达到一个距离-多普勒分辨单元;对目标的检测概率由单部雷达的28.5%提高到四部雷达协同下的83.67%。

关键词：机载雷达组网雷达空间配准信号融合联合恒虚警检测

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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration

Small Target Detection Method in Sea Clutter Based on Interf...

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IEEE International Conference on signal and Image processing (ICSIP)

作者： Shuwen Xu Yitu Ma Xiaohui Bai National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665446006

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods.

关键词： Radar cross-sections Radar clutter Surveillance Radar detection Radar Object detection Feature extraction

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基于属性散射中心卷积核调制的SAR目标识别深层网络

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雷达学报（中英文） 2024年第2期13卷 443-456页

作者：李毅杜兰周可儿杜宇昂西安电子科技大学雷达信号处理全国重点实验室西安710071

卷积神经网络(CNN)的特征提取能力与其参数量有关,一般来说,参数量越多,CNN的特征提取能力越强。但要学好这些参数需要大量的训练数据,而在实际应用中,可用于模型训练的合成孔径雷达(SAR)图像往往是有限的。减少CNN的参数量可以降低对... 详细信息

卷积神经网络(CNN)的特征提取能力与其参数量有关,一般来说,参数量越多,CNN的特征提取能力越强。但要学好这些参数需要大量的训练数据,而在实际应用中,可用于模型训练的合成孔径雷达(SAR)图像往往是有限的。减少CNN的参数量可以降低对训练样本的需求,但同时也会降低CNN的特征表达能力,影响其目标识别性能。针对此问题,该文提出一种基于属性散射中心(ASC)卷积核调制的SAR目标识别深层网络。由于SAR图像具有电磁散射特性,为了提取更符合SAR目标特性的散射结构和边缘特征,所提网络使用预先设定的具有不同指向和长度的ASC核对少量CNN卷积核进行调制以生成更多卷积核,从而在降低网络参数量的同时保证其特征提取能力。此外,该网络在浅层使用ASC调制卷积核来提取更符合SAR图像特性的散射结构和边缘特征,而在高层使用CNN卷积核来提取SAR图像的语义特征。由于同时使用ASC调制卷积核和CNN卷积核,该网络能够兼顾SAR目标的电磁散射特性和CNN的特征提取优势。使用实测SAR图像进行的实验证明了所提网络可以在降低对训练样本需求的同时保证优秀的SAR目标识别性能。

关键词：合成孔径雷达(SAR) 目标识别卷积神经网络(CNN) 属性散射中心(ASC) 卷积核调制

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