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Journal of Systems Science & Complexity 2024年第5期37卷 2196-2206页

作者： SHI Minjia XU Li SOLÉ Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China CNRS University of Aix MarseilleCentrale MarseilleI2MMarseille 13284France

Double Toeplitz(shortly DT)codes are introduced here as a generalization of double circulant *** authors show that such a code is isodual,hence formally self-dual(FSD).FSD codes form a far-reaching generalization of self-dual codes,the most important class of codes of rate ***-dual DT codes are characterized as double circulant or double ***,even binary DT codes are characterized as double *** examples obtained by exhaustive search show that the codes constructed have best-known minimum distance,up to one unit,amongst formally self-dual codes,and sometimes improve on the known *** q=2,the authors find four improvements on the best-known values of the minimum distance of FSD *** F4 an explicit construction of DT codes,based on quadratic residues in a prime field,performs equally *** authors show that DT codes are asymptotically good over ***,the authors construct DT codes arbitrarily close to the asymptotic Varshamov-Gilbert bound for codes of rate one half.

关键词： Double circulant codes double Toeplitz codes isodual codes formally self-dual codes

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Efficient Position Determination Using Low-Rank Matrix Completion 2

Efficient Position Determination Using Low-Rank Matrix Compl...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Zhang, Zehui Pu, Wenqiang Zhou, Rui You, Mingyi Wang, Wei Yan, Junkun Shenzhen Research Institute of Big Data Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Hangzhou Institute of Technology Xidian University Xi'an China National Key Laboratory of Radar Signal Processing Hangzhou Institute of Technology Xidian University Hangzhou China

ISBN: (纸本)9798331515669

In increasingly complex electromagnetic environments, distributed systems are crucial for passive target localization. These systems, consisting of spatially dispersed sensing nodes, collaboratively enhance the localization of target signals. Particularly in blind localization tasks within low signal-to-noise ratio (SNR) settings, distributed passive localization offers improved positioning performance. Traditional passive localization methods typically follow a two-step process: initially extracting parameters, such as the direction of arrivals (DOA), from raw data, followed by localizing the target. This approach often requires a high SNR. In contrast, Direct Position Determination (DPD) methods directly leverage all the received raw data, thus overcoming the constraints of the two-step process. However, DPD methods involve a time-consuming grid search within the area of interest. To address this challenge, we propose a radiation source localization method that utilizes random sampling. This method capitalizes on the low-rank properties of the grid search matrix used in DPD methods. By integrating random sampling with low-rank matrix completion algorithms, our approach efficiently localizes the radiation source. Simulation results demonstrate that this random sampling-based method significantly reduces computational demands while preserving high localization accuracy. © 2024 IEEE.

关键词： Direction of arrival

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Fast Super-Resolution DOA Estimation for Unknown Number of Radiation Sources Based on Complex Convolutional Neural Networks 2

Fast Super-Resolution DOA Estimation for Unknown Number of R...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Ma, Hui Liu, Xianpeng Liu, Hongwei Yang, Junxiang Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Xian Institute of Electromechanical Information Technology Electromechanical Dynamic Control Laboratory Xi'an China

ISBN: (纸本)9798331515669

To address the high computational complexity of traditional Direction of Arrival (DOA) estimation techniques and the limitations of previous neural networks in handling fixed signal sources, a new signal processing model was developed. This model employs sparse representation methods and deep learning through complex convolutional neural networks(CCNN). Focused research on the array's performance under unknown radiation sources using the complex convolutional neural network algorithm for super-resolution DOA estimation was conducted in a PyTorch environment. The results indicate that the complex convolutional model has a lower DOA computation complexity and higher precision in handling unknown radiation sources. After incorporating sparse representation with sampled data through the CCNN model for DOA estimation, an improvement in estimation accuracy and a reduction in computational complexity were observed compared to methods relying solely on sparse representation. Additionally, this model can effectively detect incoming waves from unknown radiation sources, unlike previous neural networks. © 2024 IEEE.

关键词： Convolutional neural networks

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FDA-MIMO beampattern optimization with complex circle manifold 2

FDA-MIMO beampattern optimization with complex circle manifo...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Qi, Jiawei Lan, Lan Liao, Guisheng Cui, Lei Chen, Guozhong Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798331515669

This paper investigates the design of beampattern in an Frequency Diverse Array (FDA)-Multiple-Input MultipleOutput (MIMO) radar by jointly optimizing the transmit waveform and receive filter considering the environment of jammers at specific range-angle intervals. In this regard, the optimization problem is formulated by forcing the constant modulus constraint on the waveform. For solving the devised non-convex problem, an alternating optimization procedure is introduced, where the optimization problem is partitioned into two subproblems related to waveform and filter, respectively. Particularly, the former is solved by utilizing a projection, descent, and retraction (PDR) method leveraging the complex circle manifold, whereas the latter resorts to a quadratic programming model. Hence, a desired beampattern with deep nulls at specific range and angle intervals can be formed. Finally, numerical results illustrate the convergence performance of the developed algorithm and the effectiveness of the devised beampattern in suppressing jammers. © 2024 IEEE.

关键词： MIMO radar

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Space-Time Adaptive processing Algorithm for EPC-MIMO radar via 3D Atomic Norm Minimization 2

Space-Time Adaptive Processing Algorithm for EPC-MIMO radar ...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Gao, Jie Zhu, Shengqi Lan, Lan Cui, Lei Chen, Guozhong Sui, Jinxin Li, Ximin Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798331515669

The issue of suppressing range-ambiguous clutter is explored with Element-Pulse Coding (EPC)-Multiple-Input Multiple-Output (MIMO) radar in this paper. In general, by incorporating a tunable coding coefficient in both slow-time pulses and transmitted elements, the range-ambiguous clutter can be distinguished from different range ambiguity regions in transmit spatial frequency dimension, and further suppressed through Space-Time Adaptive processing (STAP)-based method. In specific, to tackle the problem of insufficient Independent Identical Distribution (IID) training samples, a STAP-based method utilizing Three-Dimensional Atomic Norm Minimization (3D-ANM) is proposed to reconstruct the clutter covariance matrix (CCM). Simulations are performed to confirm the efficacy of the proposed 3D-ANM algorithm. © 2024 IEEE.

关键词： Space time adaptive processing

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A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

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Chinese Journal of Aeronautics 2022年第5期35卷 87-96页

作者： Jiaqi ZHANG Haihong TAO Xiushe ZHANG Chunlei HAN State Key laboratory of Radar Signal Processing Xidian UniversityXi’an 710068China Xi’an Research Institute of Navigation Technology Xi’an 710071China

In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each *** computational complexity can be reduced by employing the proposed *** results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.

关键词： Multi-frame track-before-detect Multiple targets detection Root label clustering State transition set Track extrapolation

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A Modified BGM-PHD Filter with Unknown Detection Probability 6

A Modified BGM-PHD Filter with Unknown Detection Probability

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

作者： Wei, Jingxin Luo, Feng Qi, Jiawei Ruan, Luyao Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Hangzhou Institute of Technology Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Hangzhou Institute of Technology Xidian University Hangzhou China

ISBN: (纸本)9798350339994

The standard Probability Hypothesis Density (PHD) filter requires manually presetting the detection probability before tracking. In addition, incorrect settings would lead to deterioration in filter performance. Beta GM-PHD filter introduces the unknown detection probability modeled by Beta distribution into the Bayes recursion to enhance the robustness under the lack of detection model parameters. However, the limitation that biases the target number and detection probability estimations significantly affects the application of the Beta GM-PHD filter. In this paper, the mechanism of the estimation biases is analyzed and a simple but effective modification is presented. Simulation results demonstrate that the modified filter provides more accurate detection probability and target state estimates in unknown and space-varying detection probability scenarios. © 2023 IEEE.

关键词： multi-target tracking parameter estimation PHD filter unknown detection probability

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Pseudo-Centroid Representation Learning for Cross-domain Few-shot Classification from Remote Sensing Imagery 2

Pseudo-Centroid Representation Learning for Cross-domain Few...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Li, Can Xie, Jianlin Chen, He Zhuang, Yin Li, Jiahao Chen, Liang Beijing Institute of Technology National Key Laboratory of Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Cross-domain few-shot classification is gaining significant attention due to its potential to address challenges in remote sensing applications, such as large domain gaps and limited labeled data. Recent approaches have explored leveraging unlabeled target domain data for semi-supervised learning, in addition to source labeled data, to improve cross-domain training. However, these techniques often struggle with getting discriminative representation for distinguishing categories in novel target domains, particularly when facing new categories and limited training examples. To overcome these issues, we propose a novel cross-domain pseudo-centroid representation (CDPCR) framework, designed to generate more discriminative and adaptable class representations for improved cross-domain learning. The framework consists of a semi-supervised cross-domain structure that integrates both source labeled data and a portion of target unlabeled data to establish the relationship between domains. A consistency regularization branch is introduced to stabilize model outputs by aligning predictions on the target unlabeled data under input perturbations. Additionally, a pseudo-centroid contrastive representation learning module is incorporated to enhance intra-category feature consistency while improving the separability of inter-category representations, aiding the model in adapting to classification tasks across diverse domains. The effectiveness of the proposed CDPCR framework is validated through comprehensive experiments conducted across 8 cross-domain scenarios using remote sensing scene classification datasets. © 2024 IEEE.

关键词： Contrastive Learning

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The Imbalanced Recognition of Space Micro-Motion Targets Based on GRB Loss 2

The Imbalanced Recognition of Space Micro-Motion Targets Bas...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Tian, Xudong Zhang, Jingang Bai, Xueru Wang, Yan Wang, Zheng Beijing Institute of Astronautical Systems Engineering Beijing China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798331515669

Limited by the quantity of space micro-motion targets launches and observation conditions, datasets for recognizing space micro-motion target usually is class imbalanced. Under such circumstances, the recognition performance of the available methods degrades rapidly, and in some cases, even complete failure. To tackle this issue, this paper proposes a gradient-guided class re-balancing loss (GRB Loss) for the imbalanced datasets of space micro-motion targets recognition. Particularly, in the proposed GRB Loss, we design an adaptive weighting function, which use the accumulated positive and negative gradient ratio of each classification node as input, to assign the weights to the positive and negative gradient, so as to improve the imbalance of the positive and negative gradient ratio in each classification node. Experimental results show that the proposed method enhances recognition performance on imbalanced datasets and remains robust across varying imbalance ratios. © 2024 IEEE.

关键词： Inverse synthetic aperture radar

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UAVs TOPS SAR Real-time Imaging System 2

UAVs TOPS SAR Real-time Imaging System

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Liu, Jing Li, Congxin Chen, Liying Deng, Yuhui Sun, Guangcai He, Chi Beijing Institute of Radio Measurement Beijing China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515669

Targeting UAVs TOPS SAR real-time imaging system, the overall system architecture, imaging algorithm and software architecture design scheme are given. Several engineering implementation problem such as Doppler center frequency correction, image distortion correction and image stitching are specifically designed. UAVs TOPS SAR is preliminarily verified by actual flight data. The rationality of SAR real-time imaging system design and effectiveness of real-time imaging algorithm are discussed. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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