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SSRN 2023年

作者： Gao, Chang Yan, Junkun Chen, Bo Varshney, Pramod K. Jia, Tianyi Liu, Hongwei National Laboratory of Radar Signal Processing Xidian University Xian710071 China Department of Electrical Engineering and Computer Science Syracuse University NY13244 United States

Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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A data-assisted adaptive detection algorithm for shortwave burst signals 5

A data-assisted adaptive detection algorithm for shortwave b...

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5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Yi Wei, Feng Wen Zhong, Qu Yan, Zhao Yu Mei, Zhou Pla Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

For shortwave signals with unique code frame structure, a data-assisted adaptive burst detection algorithm is proposed in this paper. The algorithm weakens the high bottom-noise undulation of signal correlation values due to shortwave channel fading by using a locally normalized sliding differential correlation. Also based on this, an adaptive gate limit detection strategy based on sliding window comparison judgments is used to improve the burst detection accuracy. This paper focuses on the effects of signal-to-noise ratio and shortwave channel environment on the performance of this algorithm, and simulation experiments are conducted under different conditions. The simulation results show that the algorithm has good robustness and can still achieve good detection results in the case of low signal-to-noise ratio as well as short-wave poor channels. © 2022 IEEE.

关键词： Fading channels

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A 3D Geometry Reconstruction Method for Space Targets Utilizing Improved ISAR Image Sequence Energy Accumulation

A 3D Geometry Reconstruction Method for Space Targets Utiliz...

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2021 CIE International Conference on radar, radar 2021

作者： Zhou, Zuobang Du, Rongzhen Liu, Lei Zhou, Feng Key Laboratory of Electronic Information Countermeasure and Simulation Technology Ministry of Education Xidian University Xi'an China Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665498142

As for space targets with unknown motion, the accurate projection matrix cannot be obtained in advance, which makes the three-dimensional (3D) geometry reconstruction method based on inverse synthetic aperture radar (ISAR) image sequence energy accumulation (ISEA) inapplicable. To tackle this problem, a joint factorization and ISEA-based method is proposed. Firstly, the range-Doppler measurement matrix can be formed through the steadily tracking of a small number of feature points in the ISAR image sequence. Then the factorization method is utilized to estimate the projection matrix, which reflects the projection relationship between the 3D geometry and corresponding ISAR image sequence. Next, when projected to ISAR image sequence, feature points with larger ISEA values can be regarded as the true scattering points on the geometry of the target. Therefore, all the scattering points can be reconstructed by searching the feature points with maximum ISEA values. Experimental results based on simulated point model verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： factorization method image sequence energy accumulation ISAR image sequence range-Doppler measurement matrix three-dimensional geometry reconstruction

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

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2021 International Conference on Electronics, Circuits and Information Engineering, ECIE 2021

作者： Cen, Xi Song, Xuan Li, Yachao Wu, Chunfeng Xidian University School of Life Science and Technology Xi'An China Xidian University Key Laboratory for Radar Signal Processing Xi'An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665418690

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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Algorithm for Multipath Interference Restraint Based on Blind Source Separation in Passive GNSS-Based Bistatic radar 1

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6th EAI International Conference on IoT as a Service, IoTaaS 2020

作者： Wen, Yuanyuan Bai, Lin Zhang, Xi Shang, She Song, Dawei Guo, Shuai National Key Laboratory of Science and Technology on Space Microwave Xi’an710100 China Academy of Space Information Systems Xi’an710100 China National Laboratory of Radar Signal Processing Xidian University Xi’an710070 China

ISBN: (数字)9783030675141

ISBN: (纸本)9783030675134

Passive bistatic radar belongs to passive radar systems and is a variant of bistatic radar that exploit non-cooperative‘illuminators of opportunity’ as their sources of radar transmission. Passive bistatic radar has a lot of advantages such as double-base system, silent acceptance, inherently low cost and so on, and hence attractive for a broad range of applications in recent years. The Passive GNSS-Based bistatic radar system exploits Global Navigation Satellite System as the illuminators of opportunity to detect the potential targets and is inevitable affected by multipath interference due to the reflection effect of mountains and near-earth buildings. If reference signal containing multipath interference is directly used for matching filter processing, the range-Doppler diagram will show the false target formed by matching multipath interference with echo signal. In this paper, a novel blind source separation method is proposed to recovering multipath interference from reference channel data in Passive GNSS-Based bistatic radar. The elementary reflection matrix is used as a rotation matrix to transform the cumulant matrix to realize the purpose of diagonalization. Finally the direct wave signal and multipath interference signals were separated successfully. Both theoretical analysis and simulation result verify multipath interference can be well suppressed by the proposed method. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Blind source separation

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Simultaneous radar Detection and DOA Estimation in the Presence of Unknown Mutual Coupling

Simultaneous Radar Detection and DOA Estimation in the Prese...

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IEEE National Conference on radar

作者： Massimo Rosamilia Augusto Aubry Antonio De Maio Lan Lan Department of Electrical Engineering and Information Technology University of Naples “Federico II” Naples Italy National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

This paper focuses on the joint adaptive radar detection and target Direction-of-Arrival (DOA) estimation in the presence of mutual coupling among the array elements. A suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the actual Uniform Linear Array (ULA) manifold around the nominal array looking direction. The detection problem is tackled by means of the Generalized Likelihood Ratio Test (GLRT), leveraging a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. The performance of the devised architectures is compared with benchmarks as well as with counterparts neglecting the coupling phenomenon, highlighting the capability of the proposed processing strategies to counteract the steering vector mismatches induced by the mutual counling phenomenon,

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EEMD and Double Thresholds Integrated Voice Activity Detection 3

EEMD and Double Thresholds Integrated Voice Activity Detecti...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Meng, Shan Ablimit, Mijit Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang Urumqi China Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Voice activity detection (VAD) is an important preprocessing for voice applications. Anti-noise performance is the most important evaluation index of VAD algorithm. The traditional dual-threshold-based VAD algorithm has very low detection accuracy in a low signal-to-noise ratio environment. This paper proposes a voice activity detection algorithm based on Ensemble Empirical Mode Decomposition (EEMD) combined with the dual-threshold method, which integrates the decomposition of EEMD. The denoising feature is combined with VAD based on dual thresholds, and dual thresholds are set for VAD to improve the anti-noise performance and accuracy of the algorithm. VAD is divided into three categories: based on feature parameters, based on pattern recognition, and based on deep learning. The VAD algorithms based on pattern recognition and deep learning require the support of big training data to achieve good detection results. And it is too complex and requires a large amount of computation, thus its application and real-time deployment have been greatly affected. The traditional VAD methods based on feature parameters only needs to use the short-term energy and short-term zero-crossing rate as the judgment criteria for activity detection. Thise algorithm is simple, easy to deploy, and applicable even if it is dozens of voices. At the same time, the EEMD changes the extreme point characteristics of the signal by adding different white noises of the same amplitude each time, and then performs the overall average of the corresponding IMF(intrinsic mode function) obtained by multiple EMDs to offset the added white noise, therefor effectively suppressing the mode-mixing. Thus the production of state aliasing can better improve the anti-noise performance of the VAD algorithm. © 2022 IEEE.

关键词： Empirical mode decomposition

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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A New Attitude Estimation Method of Space Target Utilizing ISAR Image Sequence under Low SNR

A New Attitude Estimation Method of Space Target Utilizing I...

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2021 CIE International Conference on radar, radar 2021

作者： Du, Rongzhen Zhou, Zuobang Liu, Lei Bai, Xueru Zhou, Feng Xidian University National Laboratory of Radar Signal Processing Xi'an China Key Laboratory of Electronic Information Countermeasure and Simulation Technology Ministry of Education Xidian University Xi'an China

ISBN: (纸本)9781665498142

Given the prior geometry model, the attitude of a space target can be estimated by evaluating the target contour similarity between the practical inverse synthetic aperture radar (ISAR) image sequence and the theoretical projection images. However, the target will be submerged in the noise under low signal-to-noise ratio (SNR), resulting in inaccurate target contour extraction and attitude estimation. In view of this, a new attitude estimation method is proposed by optimizing the projection area energy accumulation value along the ISAR image sequence. The projection area energy accumulation (PAEA) value will reach the maximum when the attitude candidate of the prior geometry model corresponds to the true one. Particle swarm optimization (PSO) algorithm is utilized to improve the search efficiency. The proposed method is more robust to the noise because of the utilization of target energy instead of the target contour. Experimental results verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： attitude estimation image sequence inverse synthetic aperture radar (ISAR) low signal-to-noise ratio projection area energy accumulation (PAEA)

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