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16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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Sixteenth International conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

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16th International conference on signal processing Systems, ICSPS 2024

ISBN: (纸本)9781510689251

the proceedings contain 197 papers. the topics discussed include: variational modal decomposition based on whale optimization algorithm for vital signs detection of FMCW radar;sea surface small target detection using tri-median normalized doppler spectrum feature;a physical cluster-based passive scatterer detection method in ISAC systems;on detection of moving weak targets using MIMO continuous active sonar and Doppler filtering;design of optical testing turntable data acquisition system based on LabVIEW;AOA /TDOA location technology of weak satellite signal based on satellite-ground cooperation;bearing-crossing localization technology for UAV target based on signal characteristics;and separation and localization of multiple bat signals using frequency domain beamforming and dual-array-localization method.

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Remote Sensing Infrared Weak and small Target Detection Method Based on Improved YOLOv5 and data Augmentation 17th

Remote Sensing Infrared Weak and Small Target Detection Meth...

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17th International conference on Intelligent Robotics and Applications

作者： Zhang, Meixin Liu, Zhonghua Zhang, Peng Yu, Qian Li, Zhiyuan Li, Yi Chinese Acad Sci CASIA Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing 100049 Peoples R China BeiJing Chengzhi Zhongke Haitu Technol Co Ltd Beijing 100195 Peoples R China Space Star Technol Co Ltd Beijing 100095 Peoples R China Beihang Univ Sch Astronaut Beijing 102206 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China

ISBN: (纸本)9789819607884;9789819607891

Remote sensing infrared satellite images have the characteristics of weak targets, insufficient contrast, and easy to be affected by the surrounding environment, such as clouds and fog, so it is a great challenge to detect weak and small targets in remote sensing. In this paper, we propose a detection method based on weak and small target enhancement, which uses a bidirectional histogram to improve the image contrast, and uses the infrared image dehazing algorithm with fog line dark primary color prior to preserve the pixel distribution of the infrared image to the greatest extent while enhancing its contrast and detail. In terms of the model, we introduce a simple and efficient weighted bidirectional feature pyramid network to optimize feature fusion, reduce redundant calculations while maintaining the detection ability of the model, and greatly reduce the memory occupation. the results show that the proposed method has achieved more competitive results than the current mainstream methods in dealing with the problem of infrared weak and small target detection, and in addition, due to the application of the weighted bidirectional feature pyramid network, the video memory is reduced by 43% while maintaining the competitive accuracy, which is of great practical significance.

关键词： Infrared weak targets YOLO Object detection

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

this letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). the median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. the three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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ESM-YOLO: Enhanced small Target Detection Based on Visible and Infrared Multi-modal Fusion 17th

ESM-YOLO: Enhanced Small Target Detection Based on Visible ...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Zhang, Qianqian Qiu, Linwei Zhou, Li An, Junshe National Space Science Center Chinese Academy of Sciences Beijing101499 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Astronautics Beihang University Beijing100191 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819609598

Detecting small targets in remote sensing imagery is frequently impeded by target faintness and complex background, resulting in reduced accuracy. this work introduces an Enhanced small Target Detection method, termed ESM-YOLO, which leverages multi-modal fusion of visible and infrared data to enhance inter-modality correlation and thereby augments performance. Firstly, we devise a pixel-level Bilateral Excitation Fusion (BEF) module to extract both shared and unique features from distinct modalities symmetrically and efficiently. Subsequently, an Improved Atrous Spatial Pyramid Pooling (IASPP) unit and a Compact BottleneckCSP (CBCSP) unit are incorporated into the detection architecture. these components are meticulously tailored to enhance the detection of minute object features, while ensuring a balance between computational efficiency and feature representation capability. Experimental results show that ESM-YOLO achieves 82.42% accuracy on the widely used Vehicle Detection in Aerial Imagery (VEDAI) dataset. the effectiveness and superiority of our proposed method are demonstrated through extensive experiments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aerial photography

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Detection and Localization of Multiple Unresolved Extended targets via Monopulse Radar signal processing

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2009年第2期45卷 455-472页

作者： Zhang, Xin Willett, Peter Bar-Shalom, Yaakov United Technol Res Ctr E Hartford CT 06108 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

When the size of targets is comparable to the range resolution of monopulse radars, these targets should be considered as extended rather than point targets. If several closely-spaced targets fall within the same radar beam and between adjacent matched filter samples in range, the full monopulse information from all of these samples can and should be used to resolve these targets, i.e., estimate the number of targets and their respective angles-of-arrival and ranges. To detect and localize multiple unresolved extended targets, we establish a model for monopulse radar returns from extended objects, and present a maximum likelihood estimator (MLE) to localize the targets. Rissanen's minimum description length (MDL) criterion will be used to decide the number of existing extended objects. We also derive the upper limit on the number of targets and their scattering centers that can be resolved, and we provide necessary conditions for these targets to be uniquely identified. We compare the new extended target monopulse processing scheme with previously developed point-target monopulse techniques in the simulations.

关键词： RADAR ELECTRONIC pulse techniques ELECTRONIC systems signal processing -- Digital techniques MACHINE theory

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Tracking of two closely spaced objects using monopulse measurements

Tracking of two closely spaced objects using monopulse measu...

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17th conference on signal and data processing of small targets

作者： Isaac, A Zhang, X Willett, P Bar-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819459186

the ultimate goal of this paper is to track two closely spaced and unresolved targets using monopulse radar measurements, the quality of such tracking being a determinant of successful detection of target spawn. It explores statistical estimation techniques based on the maximum likelihood criterion and Gibbs sampling, and addresses concerns about the accuracy of the measurements delivered thereby. In particular, the Gibbs approach can deliver joint measurements (and the associated covariances) from both targets, and it is therefore natural to consider a joint filter. the ideas are compared, and amongst the various strategies discussed, a particle filter that operates directly on the monopulse measurements is especially promising.

关键词： Radar Filtering (signal processing) Detection and tracking algorithms Particle filters Target detection Particles signal processing Monte Carlo methods signal to noise ratio Statistical analysis

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Map-enhanced tracking

Map-enhanced tracking

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17th conference on signal and data processing of small targets

作者： Sworder, DD Boyd, JE Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819459186

Tracking moving vehicles has received less attention than its its aerial counterpart. the smooth velocity transitions common to aircraft are replaced with abrupt turns and and speed changes. though the kinematic evolution of a ground vehicle is more complex, the path is more restricted. For example, if target motion is constrained by a terrain map, the topography should be integrated into the tracking algorithm. this paper shows by means of an example that the Gaussian wavelet estimator is particularly suited to map-enhanced estimation.

关键词： nonlinear filters target tracking data fusion

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Detection and localization of multiple unresolved extended objects via monopulse radar signal processing

Detection and localization of multiple unresolved extended o...

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17th conference on signal and data processing of small targets

作者： Zhang, X Willett, P Bax-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819459186

With high resolution radars, realistic objects should be considered as extended rather than point targets. If several closely-spaced targets fall within the same radar beam and between adjacent matched filter samples in range, the full monopulse information from all of these samples can and should be used for estimation, both of angle and of range (with the range to sub-bin accuracy). To detect and localize multiple unresolved extended targets, we establish a model for monopulse radar returns from extended objects, and use a maximum likelihood estimator to localize the targets. Rissanen's minimum description length (MDL) will be used to decide the number of existing extended objects. We compare the new extended target monopulse processing scheme with previously developed point target monopulse techniques to show the improvement in terms of the estimation of target locations, the detection of the number of existing targets, and the tracking performance with a multiple hypothesis tracker (MHT) using the output from the proposed extended target monopulse processor.

关键词： monopulse radar extended target unresolved target tracking

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Comparison of adaptive array processing schemes for land mine detection using hyperspectral imagery - art. no. 591301

Comparison of adaptive array processing schemes for land min...

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17th conference on signal and data processing of small targets

作者： Farrell, MD Mersereau, RM Georgia Inst Technol Ctr Signal & Image Proc Atlanta GA 30332 USA

ISBN: (纸本)0819459186

Adaptive techniques for detecting small or difficult targets in the midst of high noise and/or clutter have a rich history in the radar array processing community. However, the utility of these schemes is only beginning to be realized for multichannel electro-optical techniques, specifically hyperspectral imaging (HSI). the data products generated by hyperspectral sensors differ greatly from those of radar and sonar arrays, yet recent studies using HSI data have offered promising results for modified versions of common adaptive detectors. In this paper, we compare a series popular adaptive detection schemes applied to HSI data for the task of land mine detection. Experiments using real hyperspectral image cubes, not Simulations, are performed with data from both the visible-SWIR and LWIR regions. Results are presented for different mine types in a variety of scenes.

关键词： adaptive detection array processing hyperspectral imaging land mines

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