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检索条件"任意字段=25th SPIE Conference on Signal and Data Processing of Small Targets"

共 168 条记录，以下是1-10 订阅

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

引用

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...

引用

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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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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small object detection via fast discrete Curvelet transform

Small object detection via fast discrete Curvelet transform

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25th spie conference on signal and data processing of small targets

作者： Valdez, Hector A. Lu, thomas T. Chao, Tien-Hsin Calif State Univ Long Beach Long Beach CA 90840 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91125 USA

ISBN: (纸本)9780819497079

this paper describes the Fast Discrete Curvelet Transform (FDCT) which is integrated into an automated object detection framework capable of fast image processing for the purpose of tracking multiple objects of interest. the distinguishing difference between FDCT and other transforms is that the Curvelet coefficients have three beneficial parameters, which define scale, orientation, and spatial locations. We utilize these parameters to efficiently detect relatively small objects in a variety of image and video data sets. We have implemented the FDCT in a multi-stage automatic target recognition (ATR) architecture. the results of the current algorithm yield a highly sensitive detection of small targets in noisy backgrounds.

关键词： ATR Curvelet object detection ROI sparse representation targeting

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Identification of Human Motion Signature using Airborne Radar data

Identification of Human Motion Signature using Airborne Rada...

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25th spie conference on signal and data processing of small targets

作者： McDonald, Michael Damini, Anthony Def R&D Canada Ottawa ON Canada

ISBN: (纸本)9780819497079

data containing the radar signature of amoving person on the ground were collected at ranges of up to 30 km from a moving airborne platform using the DRDC Ottawa X-band Wideband Experimental Airborne Radar (XWEAR). the human target radar echo returns were found to possess a characteristic amplitude modulated (AM) and frequency modulated (FM) signature which could be usefully characterized in terms of conventional AM and FM modulation parameters. Human detection performance after space time adaptive processing is frequently limited by false alarms arising from incomplete cancellation of large radar cross-section discretes during the whitening step. However, the clutter discretes possess different modulation characteristics from the human targets discussed above. the ability of pattern classification techniques to use this parameter measurement space to distinguish between human targets and clutter discretes is explored and preliminary results presented.

关键词： STAP Radar GMTI

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An Examination of the Application of Space Time Adaptive processing for the Detection of Maritime Surface targets from High Altitude Airborne Platforms

An Examination of the Application of Space Time Adaptive Pro...

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25th spie conference on signal and data processing of small targets

作者： McDonald, Michael Cerutti-Maori, Delphine Def R&D Canada 3701 Carling Ave Ottawa ON Canada Fraunhofer FHR D-53343 Wachtberg Germany

ISBN: (纸本)9780819497079

An examination of the application of Space Time Adaptive processing (STAP) techniques to real, multi-channel, medium grazing angle, radar sea clutter data is undertaken and the detection performance is quantified against simulated moving maritime surface targets. the application of sub-optimal STAP approaches to the maritime radar detection problem is shown to be complicated by non-stationarity of sea clutter and rapid variations of the sea clutter spectrum due to transient wave activity. Observed performance gains from maritime STAP are much more limited than those observed for Ground Moving Target Indication (GMTI) due to the inherent spectral width of sea clutter and the slow Doppler velocities of maritime targets. three sub-optimal STAP processing architectures are examined and PRI-Staggered Post-Doppler is shown to provide consistently superior detection performance for the data set in question.

关键词： Maritime surveillance STAP Radar GMTI

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Particle Filter Tracking for the Banana Problem

Particle Filter Tracking for the Banana Problem

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25th spie conference on signal and data processing of small targets

作者： Romeo, Kevin Willett, Peter Bar-Shalom, Yaakov Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819497079

In this paper we present an approach for tracking with a high-bandwidth active sensor in very long range scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems;and most flavors of particle filter suffer from a loss of diversity among particles after resampling. this leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. the regularization is accomplished using a modified version of the Epanechnikov kernel.

关键词： Particles Particle filters Detection and tracking algorithms Filtering (signal processing) Gaussian filters Digital filtering Error analysis Sensors Active sensors Statistical analysis

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Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

Detection of unusual trajectories using multi-objective evol...

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25th spie conference on signal and data processing of small targets

作者： Smolinski, Tomasz G. Newell, Trevor McDaniel, Samantha Pokrajac, David Delaware State Univ Dept Comp & Informat Sci Dover DE 19901 USA

ISBN: (纸本)9780819497079

Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set of additive components, which then can be used to build a classifier capable of recognizing typical, but yet unseen trajectories, and identifying those that seem suspicious.

关键词： Detection of Unusual Trajectories data Mining Evolutionary Algorithms Rough Sets

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On Covariance Structure in Noisy, Big data

On Covariance Structure in Noisy, Big Data

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25th spie conference on signal and data processing of small targets

作者： Paffenroth, Randy C. Nong, Ryan Du Toit, Philip C. Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819497079

Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large- scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.

关键词： Sensors Matrices Computer networks Algorithms Computer intrusion detection Optimization (mathematics) Principal component analysis Sensor fusion data fusion Veins

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A Minimalist Approach to Bias Estimation for Passive Sensor Measurements with targets of Opportunity

A Minimalist Approach to Bias Estimation for Passive Sensor ...

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25th spie conference on signal and data processing of small targets

作者： Belfadel, Djedjiga Osborne, Richard W., III Bar-Shalom, Yaakov Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819497079

In order to carry out data fusion, registration error correction is crucial in multisensor systems. this requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. this paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. the sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

关键词： Bias estimation bias observability statistical efficiency CRLB composite measurements maximum likelihood

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