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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

引用

signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Po Zhao Jie Chen Chaochen Zhang Ke Ma Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University Hefei China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models.

关键词： Representation learning Sensitivity Prototypes Estimation Object detection Interference Feature extraction radar polarimetry Data models Synthetic aperture radar

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A Modified BGM-PHD Filter with Unknown Detection Probability

A Modified BGM-PHD Filter with Unknown Detection Probability

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IEEE International Conference on information Communication and signal processing (ICICSP)

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

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.

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Improved Cardinalized Probability Hypothesis Density Filter for Multitarget Tracking Using Formation Priors

Improved Cardinalized Probability Hypothesis Density Filter ...

引用

IET International radar Conference 2023, IRC 2023

作者： Jiang, Qi Zhang, Jichuan Ni, Na Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Real-time Information Processing Technology of Embedded Beijing100081 China

ISBN: (纸本)9781839539954

Effective observation of group targets (bird flocks, etc.) has significant importance in aviation safety and ecological research. Closely spaced group targets with swarm intelligence can form huge and complex linear formations. Tracking radar must feature a narrow beam and high bandwidth to discriminate different targets within the formation. However, there are two major problems in formation target tracking. First, the number of targets within the beam may fluctuate with the change of radar-target geometry;second, false measurements may be detected from the distributed Doppler spectra. This paper proposes a cardinalized probability hypothesis density (CPHD) filter for multitarget tracking using the formation priors form a scanning radar. The model of false measurements is optimized based on the experimental data of birds. The densities of new targets and the probability of target death are set according to the formation priors of group structure and radar-target geometry. Finally, the effectiveness of the proposed method is verified by experimental results. © The Institution of Engineering & Technology 2023.

关键词： Target tracking

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

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

引用

signal, information and Data processing (ICSIDP), IEEE International Conference on

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Training Representation learning Adaptation models Scene classification Perturbation methods Semisupervised learning Predictive models Data processing Data models Remote sensing

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Digital Coding Technology for Harmonic Control of Meta-Atoms

Digital Coding Technology for Harmonic Control of Meta-Atoms

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Xinan Hou Rui Xi Guisheng Liao National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Hangzhou Institute of Technology Xidian University Hangzhou China

ISBN: (数字)9798350383317

ISBN: (纸本)9798350383324

With the progress of meta--atoms, digital coding technology has become an important branch of this research field with its advantage in establishing the connection between digital information and meta-atoms, which greatly simplifies the metaatom design. In this paper, a digital coding technique for harmonic generation and regulation is introduced. Firstly, the harmonic number is modified by changing the phase slope, and then the corresponding harmonic amplitude and phase are regulated by designing the coding control. The introduced digital coding technique gets rid of the problem of harmonic crosstalk existing in the traditional multi-frequency control technology, and at the same time make the beam control more flexible, which greatly improves the efficiency of communication.

关键词： Costs Delay effects Crosstalk Harmonic analysis Frequency conversion Computational electromagnetics Encoding

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Movie preference prediction based on EEG signal under high brain correlation 4

Movie preference prediction based on EEG signal under high b...

引用

4th International Conference on signal processing and Computer Science, SPCS 2023

作者： Yue, Yuanhao Zhao, Xingcong Zhou, Wei Liu, Guangyuan School of Electronic and Information Engineering Southwest University Tiansheng Road Beibei Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China

ISBN: (纸本)9781510672505

In this study, we recorded the electroencephalography (EEG) of human subjects when they watched a movie trailer. Based on the willingness preference from the subjects after finished the movie trailer watching, the EEG signals were analyzed to build regression models to predict the subjects' movie preferences. We proposed a new approach to rise the prediction accuracy. Specifically, the correlated component analysis (CCA) algorithm was used to calculate the inter-subject correlation (ISC) of each time segment in the movie trailer and the ISC of each channel in the EEG. These two states were selected to locate the moment when a specific area of the brain responds most strongly to the video stimulation, as an effective indicator for predicting individual movie preferences. The experimental results show that the method of selecting effective EEG signals at the level of ISC has achieved a better prediction effect on the GBDT regression model (R2 = 0.733, RMSE = 0.681). We also found that low-frequency EEG signals in the θ and δ bands of the parietal and occipital sites are more effective in predicting movie preferences than high-frequency EEG signals. © 2023 SPIE.

关键词： Electroencephalography

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A Fast Blind Deblurring Algorithm Using Local Gradient Product Prior

A Fast Blind Deblurring Algorithm Using Local Gradient Produ...

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jixuan Liang Yanshan Li College of Electronic and Information Engineering Shenzhen University Shenzhen China Institute of Intelligent Information Processing Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Blind image deblurring is the restoration of latent clear images from blurred images without knowing the blur kernel. Recently, a large number of priors have been proposed to effectively address the ill-posed nature of blind deblurring. However, most methods approximate the proposed priors and only use the first-order term of the blurred image. We observe the gradient inner product of clear images is significantly larger than that of blurry ones. In this paper, we propose a novel Local Gradient Product (LGP) prior based on this observation. This prior not only offers a more accurate approximation but also uses the relationship between image pixels involving quadratic terms. The employment of the LGP prior avoids the inversion of large matrices, thereby improving solution efficiency. Extensive experiments demonstrate that our algorithm achieves state-of-the-art performance and competitive running speed on benchmark datasets. Our MATLAB code and experimental results are available at ***/JixuanLiang/deblur-LGP.

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A Novel Sparse Frequency radar Waveform Employing Multi-frequency Component processing

A Novel Sparse Frequency Radar Waveform Employing Multi-freq...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xiaohe Du Shenghua Zhou Jian Bai Jingshu Qin Aihua Li National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Traditional radar systems typically use wideband signals but have limited anti-jamming capabilities. Sparse frequency waveforms offer a solution to narrowband interference issues. However, existing sparse frequency waveform design methods often handle the entire signal uniformly, resulting in fixed frequency notch positions and limited waveform flexibility. In this paper, we propose a new method for optimizing sparse frequency waveforms. While maintaining constant waveform pulse width and range resolution, we increase the degree of freedom by extending the length of phase coded signals. Pulse compression is applied to each frequency band component with the objective of minimizing sidelobes, using the minimax algorithm based on sequential quadratic programming (SQP) to obtain optimal phase coded waveforms. Simulation results demonstrate the method’s capability to flexibly design sparse frequency waveforms and reduce pulse compression sidelobe levels.

关键词： Simulation Design methodology radar detection radar Interference radar signal processing Reliability Quadratic programming Wideband signal resolution

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

引用

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

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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

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

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signal, information and Data processing (ICSIDP), IEEE International Conference on

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Target recognition Data processing Inverse synthetic aperture radar

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