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16th International Conference on signal processing Systems, ICSPS 2024

作者： Liu, Xuchen Liu, Yongjun Lu, Jiaguo Liao, Guisheng Zhang, Daisheng Tang, Hao Wang, Heming Dong, Xiaoyang National Key Laboratory of Radar Signal Processing Xidian University China The 38th Research Institute of China Electronics Technology Group Corporation China Jianghuai Advanced Technology Center China

ISBN: (纸本)9781510689251

In the dual-function radar-communication (DFRC) system, the radar and communication waveforms will be distorted and the signal-to-interference-plus-noise ratio (SINR) of DFRC system and downlink user will decrease when the mutual interference between radar and communication is not suppressed. To this end, a joint design method of transmit waveform and receive weight is proposed in this paper. Firstly, the signal model involving the DFRC system and the downlink user is developed. Then, the joint design problem of transmit waveform and receive weight is formulated. In this problem, the radar and communication SINR is maximize with the energy constrains. To solve the problem, the joint design method based on the block coordination descent (BCD) framework is proposed. Finally, the effectiveness of the proposed joint design method is verified via simulation results. © 2025 SPIE.

关键词： signal to noise ratio

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signal Fusion-based Distributed Detection in Heterogeneous radar Scenarios

Signal Fusion-based Distributed Detection in Heterogeneous R...

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2023 IEEE International radar Conference, radar 2023

作者： Wang, Aoya Zhou, Shenghua Zhang, Man Wang, Ziwei Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9781665482783

In distributed radar system, local radar may form heterogeneous statistics which probability density functions (PDFs) are different, but it is typically difficult to combine such observations for better detection performance. In this paper, taking fusion of the cell-average constant false alarm rate detector and the square-law detector for instance, we propose a signal-fusion based distributed detection algorithm and its global test statistic is a sum of linearly weighted local statistics, which the local weights depend on the channel SNRs. According to the Neyman-Pearson criterion and under the assumption of independence of local observations, the optimal test statistic is a sum of complicated functions of local test statistics and thus its PDFs are difficult to obtain. We first find the equivalent local test statistics to ensure that the PDF of a sum of the equivalent local test statistics under the null hypothesis has a closed-form expression. Then we approximate the global test statistic by the first-order Taylor series of the local likelihood ratio functions and local test statistics are properly weighted. Numerical results show that the detection performance of the proposed method outperforms the decision fusion algorithm. It is also found that the proposed algorithm is robust to SNR estimation errors. © 2023 IEEE.

关键词： signal to noise ratio

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Subarrayed array thinning using iterative FFT

Subarrayed array thinning using iterative FFT

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IET International radar Conference 2023, IRC 2023

作者： Liu, Bingfan Li, Zhenyu Chen, Baixiao Gao, Bing Zhang, Huaigen Nanjing Research Institute of Electronics Technology Nanjing China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781839539954

This paper proposes a fast-Fourier-transform-based iterative technique for the synthesis of a large thinned planar array with a subarray. Unlike array thinning at the element level, for array thinning at the subarray level, the overlapping of subarrays should be examined. In each iteration, the proposed algorithm is applied to the set of all possible subarrays to select some nonoverlapping subarrays with excitation similar to the expected excitation. The selected subarrays constitute the optimal thinned array with the lowest sidelobe level. Numerical experiments involving a thinned planar array with regular and irregular subarrays showed the effectiveness of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Numerical methods

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Distributed radar Target Detection with Ordered Local Statistics

Distributed Radar Target Detection with Ordered Local Statis...

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2023 IEEE International radar Conference, radar 2023

作者： Zhang, Man Dou, Fabing Zhou, Shenghua Wang, Ziwei Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9781665482783

In distributed radar with local radar sites using cell-average constant false alarm rate (CA-CFAR) algorithm, the signal fusion based detection algorithm has been studied. However, the order statistic (OS) CFAR algorithm, as another important detection algorithm, has not been considered in distributed detection with signal fusion. This paper presents two algorithms for distributed detection with OS-CFAR test statistics. The first algorithm is that it randomly selects multiple values of order statistics to estimate the noise power through the maximum likelihood estimation (MLE) criterion. The second algorithm selects one of order statistics to estimate the noise power, and the optimum detection statistic for the fusion center is developed based on the Neyman-Pearson criterion. In numerical results, an iterative method is used to estimate the decision thresholds and the location of the selected value in order statistic has been verified to have a significant impact on detection performance. In particular, performance of the optimum detection statistic in distributed OS-CFAR is superior to suboptimum statistic and nonparametric statistic when choosing value at the front of the order statistics, while the conclusion is opposite when choosing value at the end of the order statistics. © 2023 IEEE.

关键词： Iterative methods

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Robust Scattering Center Extraction Method for InISAR Imaging Based on Range-Azimuth Profile Reconstruction 6

Robust Scattering Center Extraction Method for InISAR Imagin...

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6th International Conference on Information Communication and signal processing, ICICSP 2023

作者： Kang, Hailong Zhao, Haojie Xu, Liang Li, Jun Hangzhou Institute of Technology Xidian University Hangzhou China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350339994

The extraction of the scattering center is a key step in the 3D imaging of interferometric inverse synthetic aperture radar (InISAR). Due to the influence of background noise and sidelobes, the existing methods often lead to inaccurate extraction of scattering center, resulting in outlier or loss of point clouds in the reconstructed 3D images. To solve this problem, we propose a robust scattering center extraction method based on range-azimuth profile reconstruction. First, the 1D range profile and the 1D azimuth profile are reconstructed based on autocorrelation matrix eigenvalue decomposition to suppress the background noise, and then the threshold is determined based on the proposed threshold updating mechanism to extract the scattering center accurately. Finally, the effectiveness and superiority of the proposed method are verified by the simulation results. © 2023 IEEE.

关键词： 1D range profile interferometric inverse synthetic aperture radar (InISAR) scattering center extraction threshold updating

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High-Speed Maneuvering Target Detector Based On Deep Residual Network

High-Speed Maneuvering Target Detector Based On Deep Residua...

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IET International radar Conference 2023, IRC 2023

作者： Jiang, Wenqi Jiu, Bo Zhao, Yu Liu, Hongwei Zhang, Yu Wang, Chunlei National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Nanjing Research Institute of Electronics Technology Nanjing China

ISBN: (纸本)9781839539954

Range migration and Doppler frequency migration lead to severe coherent integration loss and worse target detection performance in high-speed maneuvering target detection. In this paper, a deep residual network detector (RND) is proposed for high-speed maneuvering target detection. The RND is used to replace the Doppler processing and hypothesis testing components in the conventional signal processing procedure. By training, the RND is capable of effectively achieving a nonlinear mapping from range-compressed signals to target detection results, demonstrating that the proposed detector exhibits excellent detection performance. Finally, numerical experiments validate that the detection performance of the RND is better than that of the KT-MF algorithm in the scenario of higher signal-to-noise ratio, and the computational complexity is much lower than that of the KT-MF algorithm, which can demonstrate the RND can achieve a trade-off between detection performance and computational complexity. © The Institution of Engineering & technology 2023.

关键词： signal to noise ratio

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A weighted low-rank and sparse constraint-based multi-channel radar forward-Looking imaging Method

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12973-12987页

作者： Tang, Junkui Ran, Lei Zheng, Liu Xie, Rong Liu, Yan Han, Genquan Xidian University Xi'an7100071 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China Xidian University School of Microelectronics Xi'an7100071 China

radar super-resolution imaging method with joint low-rank and sparse constraints has garnered increasing attention. However, in complex imaging scenarios, the low-rank property of the signal matrix is often not prominent, which limits the performance of directly applying low-rank constraints in super-resolution imaging. To address this issue, this paper proposes a weighted low-rank and sparse (WLRS) constraint-based multi-channel radar forward-looking super-resolution imaging method. First, the proposed method calculates weighting coefficients using the covariance matrix of radar echoes and applies weighted constraints to the signal matrix, thereby enhancing its low-rank property and significantly improving forward-looking super-resolution imaging performance. Then, in solving the optimization problem, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose variables and reduce the complexity of the solution. To further enhance computational efficiency, the symmetry of the weighting matrix and the characteristics of the dictionary matrix in sparse imaging, specifically as a partial Fourier dictionary, are leveraged. A fast matrix inversion method based on eigenvalue decomposition is proposed to mitigate the computational complexity induced by super-resolution requirements. Finally, the effectiveness and superiority of the proposed method in complex scenarios are validated through comparative experiments on simulated and measured data. © 2008-2012 IEEE.

关键词： Eigenvalues and eigenfunctions

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Medical image segmentation based on adaptive threshold prediction and feature fusion

Medical image segmentation based on adaptive threshold predi...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Liu, Wei Mo, Jiaqing Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510677685

In the realm of computer-aided diagnosis, image segmentation techniques have garnered significant attention over the years. However, medical image data present distinctive characteristics, such as confidentiality, class imbalance, and challenges in acquiring accurate labels. Of certain rare medical conditions, it is arduous to obtain an adequate number of data samples within a limited scope for training models. Moreover, complex medical conditions may lead to diagnostic omissions concerning the locations of minor lesions. Consequently, our primary objective in this article is to address the challenge of enhancing the segmentation accuracy of lesion locations in complex medical conditions when working with limited data. Additionally, we believe that threshold selection and feature choice are particularly crucial for medical image segmentation tasks. However, existing medical image segmentation methods often prioritizes model complexity while overlooking the impact of threshold variations on segmentation performance. In this paper, we propose a multi-feature fusion network with adaptive threshold prediction and introduce a novel paradigm of semi-supervised pseudo-label generation to facilitate supervised learning. The multi-feature fusion module ensures the comprehensive utilization of dimension-sensitive features, while the adaptive threshold predictor enables the model to generate distinct segmentation thresholds based on varying inputs. We employed publicly available medical image datasets, namely Kvasir-SEG, Rim-one r2, and ISIC 2018, to train and validate our model and approach. In comparison to other supervised and semi-supervised methods, the model and the approach we proposed demonstrate superior segmentation performance. © 2024 SPIE.

关键词： Computer aided diagnosis

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Joint Motion Compensation radar Imaging Based on Bi-XLSTM and BP Algorithm

IEEE Transactions on Radar Systems

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IEEE Transactions on radar Systems 2025年 3卷 738-755页

作者： Ren, Xuemei Li, Xiaoyong Rong, Pengshuai Zhou, Wanting Liu, Lei Bai, Xueru Zhou, Feng Xidian University National Key Lab of Radar Signal Processing Xi’an710071 China Xidian University Hangzhou Institute of Technology Hangzhou311231 China Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xidian University School of Aerospace Science and Technology Xi’an710071 China

Advances in high-speed digital acquisition and storage technologies have enabled the sampling of radar echoes at intermediate frequencies. Precise gate control and recording techniques facilitate translational compensation based on echo coherence, enabling robust imaging even in low SNR conditions. Despite these advancements, existing approaches face inefficiencies in parameter estimation and encounter significant difficulties in azimuth focusing when increasing imaging resolution at large angles. To address these challenges, a novel joint motion compensation imaging algorithm that integrates Bidirectional Extended Long Short-Term Memory (Bi-XLSTM) networks with back projection (BP) is proposed. This approach initiates with the development of a comprehensive joint motion echo model for the target to reduce cumulative errors, thereby establishing a solid foundation for further joint motion compensation. Next, Bi-XLSTM is utilized for the efficient extraction of motion parameters from coherent echoes. The Adam optimization algorithm is then applied during the BP imaging process to jointly optimize motion and rotation parameters, targeting optimal image quality and resulting in highly focused images. Experiments conducted on point simulations, electromagnetic simulations, and real data confirm that this technique outperforms traditional methods. © 2023 IEEE.

关键词： Motion analysis

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