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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 ...

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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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AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR SHIP DETECTION IN SAR IMAGES

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR S...

引用

IET International Radar Conference 2023, IRC 2023

作者： Chen, Fan Shi, Hao Zhao, Liangbo Mao, Yongfei Pan, Hongxin Chen, Liang Radar Research Lab Beijing Institute of Technology Beijing China General Department of Remote Sensing Satellites China Academy of Space Technology China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Since entering the era of deep learning, the single-stage detection algorithm represented by YOLO has achieved some progress in the detection of ship targets by synthetic aperture radar (SAR). However, the accuracy of single-stage detection is lower than that of two-stage detection, especially for small target detection. To this end, this paper proposes an improved YOLOv5 detection method based on convolutional bolck attention module CBAM) and WPAN. At the same time, the focal efficient intersection over union (Focal EIoU) is introduced to optimize the calculation of bounding box regression loss. The experiment is verified on SAR ship detection dataset(SSDD). The results show that the proposed improved YOLOv5 algorithm can improve the false alarm and missing detection problems in multi-scale target detection, and improve the detection accuracy consequently. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Dual-Level Contrast Enhancement Method for Infrared Small Target Detection

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

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

作者： Haotian Sun Wenzheng Wang Yuhan Xu Qiuyu Jin Zipeng Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds; however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. Small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target’s SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target’s radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results.

关键词： Filters Simulation Object detection Interference Reconnaissance Robustness Windows Noise measurement Signal to noise ratio Visual perception

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A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATURES IN ENTOMOLOGICAL RADAR

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATU...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Biao Liu, Hanzhe Zhang, Tianran Cai, Jiong Wang, Rui The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

When tracking densely distributed targets such as insects, the traditional trajectory association algorithm exhibits poor correlation performance, leading to a decline in multi-target tracking efficiency. In this paper, based on the behavioral characteristics of insects gathering and co-orienting during aerial flight, a motion dynamic feature-assisted multi-target tracking method is proposed. Firstly, a three-dimensional biological distribution state matrix is constructed through spatial gridization. Then, a three-dimensional circular convolution is employed to estimate the target's motion dynamic feature, assisting in multi-target trajectory initialization. Finally, the improvement effect of the method on trajectory tracking accuracy is validated through simulations and migration insect data. © The Institution of Engineering & technology 2023.

关键词： Radar tracking

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Open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity 2

Open Set Radar HRRP Recognition Using Confidence through Neu...

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

作者： Liu, Yichen Liu, Ping-An Zhang, Liang Wang, Yanhua Bao, Zengdi Xu, Yinhui Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China

ISBN: (纸本)9798331515669

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions. © 2024 IEEE.

关键词： Radar target recognition

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DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PROCESSOR IN ONBOARD SAR IMAGING SYSTEM BASED ON FPGA

DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PRO...

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IET International Radar Conference 2023, IRC 2023

作者： Xu, Ming Zhang, Jiawei Li, Yongrui Yin, Yifei Zhang, Ao Dong, Heng Chen, Liang Shi, Hao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai201109 China Sichuan TianFu New Area Beijing Institute of Technology Innovation Equipment Research Institute Chengdu610299 China

ISBN: (纸本)9781839539954

Spaceborne Synthetic Aperture Radar (SAR) plays an important role in the field of emergency disaster reduction. In order to meet the stringent timeliness requirements in onboard SAR data processing, this paper proposes a configurable four-channel parallel FFT processor, which serves as the core of SAR imaging processing. This processor is capable of supporting up to 128K-point FFT operations. Additionally, it supports radix-2 time decimation FFT concatenation processing, radix-4 time decimation FFT concatenation processing, radix-2 frequency decimation FFT concatenation processing, and radix-4 frequency decimation FFT concatenation processing, all of which can be flexibly configured based on data structure. This processor is implemented on the Xilinx Virtex-7 XC7VX690T FPGA and can achieve a maximum FFT processing capability of 128K points at a frequency of 300MHz. The evaluation and analysis results demonstrate that the processor reaches a higher processing rate, effectively meeting the demanding requirements for efficient spaceborne processing. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries 2

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

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

作者： Yu, Binhua Liu, Ping-An Liu, Yichen Wang, Hongyu Zhang, Liang Wang, Yanhua Liu, Kun Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target's pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method. © 2024 IEEE.

关键词： Radar imaging

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation 2

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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

作者： Zhang, Zhilong Wu, Heke Xu, Yinhui Zhang, Liang Wang, Yanhua Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 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

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters. © 2024 IEEE.

关键词： Convolutional neural networks

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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method 2

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

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

作者： Meng, Yuanhao Wang, Lue Zhou, Qiang Zhang, Xin 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 Modern Control Technology Research institute 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

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously. © 2024 IEEE.

关键词： Radar target recognition

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Multi-Temporal Images Generation for Building Change Detection Performance Promotion

Multi-Temporal Images Generation for Building Change Detecti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yute Li Wei Li Nan Wang Chenzhong Gao Yin Zhuang He Chen Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The changes in building are important basis for urban monitoring. However, due to the rarity and sparsity of the occurrence of changes in buildings, collecting effective bitemporal image pairs is challenging, as it requires long-term observation over several months or even years. Additionally, annotating large-scale change detection datasets is time-consuming and labor-intensive. Consequently, data scarcity issues lead to insufficient training of building change detection models. To address this, we propose a data generation method Building Generation GAN (BG-GAN). Different from other GANs, the BG-GAN is trained based on adversarial consistency loss, enabling the model to generate new bi-temporal image pairs with various types of building changes. To verify the effectiveness of the proposed methods on change detection task, BG-GAN is utilized to perform building change samples generation on two building change detection datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that the proposed method can improve the robustness and generalization of change detection model to detect pseudo changes.

关键词： Training Accuracy Image synthesis Buildings Geoscience and remote sensing Generative adversarial networks Data models

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