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检索条件"任意字段=18th Conference on Signal and Data Processing of Small Targets"

共 390 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...

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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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EMG signal Classification Using Machine Learning for Augmentative Assistance in Cerebral Palsy 18th

EMG Signal Classification Using Machine Learning for Augment...

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18th International KES conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Ramirez-Lopez, Neri Marquez, Bogart Yail Alanis, Arnulfo Magdaleno-Palencia, Jose Sergio Departamento de Sistemas y Computación Maestría en Tecnologías de Información Tecnológico Nacional de México Campus Tijuana Tijuana Mexico

ISBN: (纸本)9789819764686

this article presents a research project that aims to assist individuals with cerebral palsy to improve their communication abilities and achieve greater autonomy using electromyography (EMG) technology. EMG records the electrical activity of muscles, providing insights into muscle physiology and activation. the proposed system would filter EMG signals, adapt them to the user's needs, store data, and present it through a graphical interface. the system targets those with cerebral palsy, facing challenges performing body movements and communication. the article provides background on EMG and its applications, especially for those with disabilities. It outlines common motor impairments in cerebral palsy. the methodology involves classified EMG signals over time. Results demonstrate the superior performance of random forests over single decision trees for prediction. Overall, the project leverages EMG technology to enhance communication and autonomy for cerebral palsy patients. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electromyography

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Personalized mmWave signal Synthesis for Human Sensing 18th

Personalized mmWave Signal Synthesis for Human Sensing

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jiaqi Liu, Hankai Xie, Xin Tong, Xinyu Liu, Xiulong Li, Keqiu College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9783031714665

the excellent sensing performance and privacy-preserving nature of millimeter-wave (mmWave) radar has led to its increasing prominence in the field of human sensing, enabling it to support a wide range of human sensing applications. However, the scarcity of mmWave datasets and the high cost of data collection limit the capability of deep learning. In this paper, we propose PmSyn, a system that synthesizes mmWave signals based on human body model provided by public human motion database and trains neural networks with signatures extracted from synthesized signals to achieve activity recognition and skeleton tracking. We analyze in depth the reflection of the human body model and the propagation principle of the signal. We propose to inject real user’s shape information into the public human body model to endow the synthesized signals with personalization. Experiments show that for activity recognition, PmSyn achieves 93.1%/98.6% accuracy under five activities when trained with synthesized data only/adding a small amount of real data. For skeleton tracking, PmSyn achieves an overall skeleton tracking error of 10.5 cm/6.2 cm when trained with synthesized data only/adding a small amount of real data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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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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PHD filters of second order in target number

PHD filters of second order in target number

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18th conference on signal and data processing of small targets

作者： Mahler, Ronald Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819462926

the multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In an earlier paper at this conference we derived a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number. Since the CPHD filter has computational complexity O(m(3)) in the number m of measurements, additional approximation is desirable. In this paper we discuss a second-order approximation called the "binomial filter."

关键词： Digital filtering Nonlinear filtering Sensors Target detection Electroactive polymers Filtering (signal processing) Motion models Picosecond phenomena Electronic filtering Linear filtering

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Peak inspecting and signal recovery methods based on triple correlation - art. no. 623608

Peak inspecting and signal recovery methods based on triple ...

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18th conference on signal and data processing of small targets

作者： Cao Hanqiang Li Xutao Chen Rujun Wei Jiaxin Cao Chao Huazhong Univ Sci & Technol Dept Elec & Info Eng Wuhan 430074 Peoples R China

ISBN: (纸本)0819462926

Weak target inspecting and recovering are very important in IR detecting systems. In this paper, triple correlation peak inspecting techniques(TCPIT) are adopted for the signal processing of IR systems in detecting sub-pixel or point targets. Investigations show that the signal-to-noise ratio (SNR) improvement of approximate 23dB can be obtained with the input peak SNR of 0.84 and the input power SNR of -0.93dB. the triple correlation overlapping sampling technique(TCOST) is advanced for restoring signal waveforms of IR detection systems. Investigations show that signal waveforms can effectively be restored in the low signal-to-noise ratio circumstances using this approach.

关键词： triple correlation weak signal inspecting detection of point target IR detecting system

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Fixed-lag sequential Monte Carlo data association

Fixed-lag sequential Monte Carlo data association

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18th conference on signal and data processing of small targets

作者： Briers, Mark Doucet, Arnaud Maskell, Simon R. Horridge, Paul R. QinetiQ Ltd. (United Kingdom) Cambridge Univ. (United Kingdom) The Univ. of British Columbia (Canada)

ISBN: (纸本)0819462926

the use of multiple scans of data to improve ones ability to improve target tracking performance is widespread in the tracking literature. In this paper, we introduce a novel application of a recent innovation in the SMC literature that uses multiple scans of data to improve the stochastic approximation (and so the data association ability) of a multiple target Sequential Monte Carlo based tracking system. Such an improvement is achieved by resimulating sampled variates over a fixed-lag time window by artificially extending the space of the target distribution. In doing so, the stochastic approximation is improved and so the data association ambiguity is more readily resolved.

关键词： particle filter Sequential Monte Carlo Multi-target tracking fixed-lag data association out-of-sequence measurements

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WALL-CLUTTER MITIGATION USING CROSS-BEAMFORMING IN thROUGH-thE-WALL RADAR IMAGING

WALL-CLUTTER MITIGATION USING CROSS-BEAMFORMING IN THROUGH-T...

引用

18th European signal processing conference (EUSIPCO)

作者： Debes, Christian Weiss, Christian Zoubir, Abdelhak M. Amin, Moeness G. Tech Univ Darmstadt Signal Proc Grp Darmstadt Germany Villanova Univ Ctr Adv Commun Villanova PA 19085 USA

A new technique for removal of the wall EM returns in through-the-Wall Radar Imaging is presented. It is based on the spatial notch-filtering to separate wall and target reflections. the proposed technique forms squint beams at the receiver using a divided aperture. In doing so, it removes the strong wall signature without eliminating those of the targets. the proposed scheme provides desirable 3D target detection which is evaluated using real data examples from through-the-Wall radar imaging experiments.

关键词： Beamforming

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Comparison of tracklet methods with deterministic dynamics and false signals - art. no. 62360Z

Comparison of tracklet methods with deterministic dynamics a...

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18th conference on signal and data processing of small targets

作者： Drummond, Oliver E. Dana-Bashian, David CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)0819462926

Track fusion processing is complicated because the estimation errors of a local track and a fusion track for the same target are usually cross-correlated. If these errors are cross-correlated, that should be taken into account when designing the data association processing and the filter used to combine the track data. An approach to dealing with this cross-correlation is to use tracklets. One of the important issues in tracklet fusion performance is whether the dynamics are deterministic, e.g., no filter process noise and no target maneuver. A number of different tracklet methods have been designed. this paper presents a comparison a tracklets-from-tracks approach to a tracklets-from-measurements approach. Tracklet fusion performance is also compared to centralized measurement fusion performance. the emphasis is on performance with targets that exhibit deterministic dynamics and the possibility of measurements caused by false signals.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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