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Tiny Target Detection with Adaptive Weighted Feature Fusion in UAV Images 2

Tiny Target Detection with Adaptive Weighted Feature Fusion ...

引用

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

作者： Kang, Xiaohui Zheng, Lingjie Deng, Zhiyuan Han, Yuqi Tang, Linbo Deng, Chenwei Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan250300 China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

With the development of drones, imaging lenses and other equipment, effective and accurate detection of tiny targets in drone remote sensing images has become a hotspot. However, the features of tiny targets are weak and difficult to extract. Most existing methods enhance the target feature expression by directly superimposing and fusing multi-scale feature maps. Only the features with stronger responses in adjacent feature maps can be enhanced, while the targets with weaker responses may be diluted. For this reason, an adaptive weighted fusion small target detection algorithm is designed. To enhance the proportion of small targets in feature maps of different resolutions, a bidirectional feature pyramid network is designed. The adaptive weights are set according to the feature density in the feature map, and they will be used to perform bidirectional fusion on feature maps to reduce the interference of background and large targets on the features of small targets. Besides, we design multi-scale dilated convolution, which is injected into the feature fusion network. In this way, we successfully enhance the expression of small target feature. Finally, we test the algorithm via the public dataset VisDrone, the results show that our methods significantly improve the accuracy of small target detection. © 2024 IEEE.

关键词： Drones

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Robust Adaptive OS-CFAR Detector for Maneuvering Group Target Based on Formation Prior Knowledge 2

Robust Adaptive OS-CFAR Detector for Maneuvering Group Targe...

引用

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

作者： Shi, Mengxin Jiang, Qi Wang, Rui Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (纸本)9798331515669

Detecting the individuals such like the bird flocks or a UAV swarm is a great challenge for radar systems. The lack of prior knowledge about the characteristics of dynamic group targets leads to the performance degradation of conventional detectors. In this study, we present a robust adaptive OS-CFAR detector for maneuvering group targets, leveraging prior knowledge of their formation. The group target's state is updated and predicted using the random matrix approach. The projection length of the group target in the radar beam direction is computed using the predicted information during the group target tracking process. Using the projection length, the number of reference units occupied by the group target within the reference window is determined, and this prior knowledge is applied to update the detection parameters of the OS-CFAR detector. The simulation result shows that the algorithm can update the detection parameters timely and avoid target missed detection. © 2024 IEEE.

关键词： Radar tracking

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Improved multiple hypothesis tracking using incomplete measurements for group targets 2

Improved multiple hypothesis tracking using incomplete measu...

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

作者： Ni, Na Jiang, Qi Hu, Cheng Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China Beijing Institute of Technology The Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

Tracking targets within a group has received much attention in the radar field recently years. Group targets are usually closely spaced with similar velocity, making it hard to distinguish multiple targets. For precise target tracking, the tracking radar usually employs a narrow beam to achieve the high range-angular resolution. However, targets may enter and exit the narrow beam frequently during the observation, bringing difficulty for stable tracking. Moreover, it is also hard to distinguish different targets when their tracks intersect on the range-doppler plane. These kinds of incomplete measurements increase the difficulty of group targets' association and tracking. Therefore, this paper proposes an improved multiple hypothesis tracking method using incomplete measurements. The adaptive detection probability model and track intersecting probability are combined in the multiple hypothesis tracking framework to reduce false associations and improve tracking performance. Finally, simulation and experiment results verified the effectiveness of the proposed method. © 2024 IEEE.

关键词： Target tracking

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An Adaptive Estimation Method for Group Target Number Based on information Theoretic Criterion 2

An Adaptive Estimation Method for Group Target Number Based ...

引用

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

作者： Dou, Libin Jiang, Qi Wang, Rui Shi, Mengxin Zhang, Jichuan Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China Beijing Institute of Technology The Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

The number of individuals within a group is crucial information for the correct association, robust tracking, and situation awareness. Traditional methods using the range profile's peaks are not suitable when targets are densely distributed. The target number estimation algorithms based on monopulse systems have high computational complexity, while those based on array systems require additional sensor resources. To address these issues, this paper proposes a method based on multiple incoherent observations using one monopulse radar. By combining multiple incoherent observations from a single sensor with information theoretic criterion, this approach can estimate the number of targets even when the resolution of a single sensor is insufficient. The effectiveness of the algorithm is verified through simulations, and the algorithm's performance is analyzed. © 2024 IEEE.

关键词： Monopulse radar

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Labeled Random Finite Sets-Based Group Target Tracking with Multi-Detection Model 2

Labeled Random Finite Sets-Based Group Target Tracking with ...

引用

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

作者： Jiao, Longxiang Jiang, Qi Wang, Rui Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China Beijing Institute of Technology The Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

In radar tracking applications for low-altitude group targets (such as bird flocks or drone swarms), the traditional point target measurement model is often unsuitable due to the complex micro-Doppler signatures and multipath effects. Algorithms based on labeled random finite sets (RFS) challenges with multi-detection problems. This paper presents a new method for tracking low-altitude group targets using labeled RFS in radar systems. To mitigate the disturbance from multi-detection, we propose modified assignment cost matrices in the update process, which reduces the number of false associations, and thus achieves quality enhancement of trajectory estimations. The effectiveness of the proposed method is validated through simulation results. © 2024 IEEE.

关键词： Radar tracking

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Design of SAR Imaging System Based on MPSoC + FPGA with Optimization Strategies for Chirp Scaling Factor Computation

Design of SAR Imaging System Based on MPSoC + FPGA with Opti...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhong, ZhiHui Qiao, TingTing Xie, YiZhuang Lv, HuShan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Space-based remote sensing involves complex SAR imaging hardware design. This complexity arises from varied imaging modes and a convoluted imaging process. With the rapid advancement of embedded systems and Field Programmable Gate Arrays (FPGA), underscores their flexibility, efficiency, and reconfigurability for SAR imaging hardware implementation. This paper introduces a comprehensive SAR imaging system based on Multi-Processor System on Chip (MPSoC) and FPGA architectures. The system supports a multi-modal SAR imaging processing platform with large granularity, encompassing orbital parameter pro-cessing, Chirp Scaling (CS) factor computation, data transposition, complex multiplication, and FFT operations. An effi-cient data link between the host computer, MPSoC, and FPGA is also established. To address the challenges of multiple parameters, intricate computational flow, and various computational types in Chirp Scaling factor generation, the system utilizes Cortex A53 and Cortex R5 processors for computation and optimization. The results indicate an average accelera-tion ratio of 25.296, a relative error of 1.12×10-4, and an absolute error of 5.88×10-6, demonstrating that the "MPSoC+FPGA"-based imaging system meets the demands of imaging processing. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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realization of Remote Sensing Image Classification Algorithm Based on SOPC

Realization of Remote Sensing Image Classification Algorithm...

引用

IET International Radar Conference 2023, IRC 2023

作者： Qi, Guijie Qiao, Tingting Xie, Yizhuang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Recently, Convolutional Neural Networks (CNNs) have made significant progress in the field of remote sensing image scene classification. However, commonly used CNN models are mainly deployed on GPU, which is bulky and power-hungry, making them unsuitable for customized requirements of on-board embedded applications. To meet the needs of on-board embedded applications, this study selects System On a Programmable Chip (SoPC), known for their low power consumption and compact size, as the processing platform for accelerating CNNs. Employing the knowledge distillation method, this paper uses the classical CNN scene classification algorithms to create lightweight models. These optimized models are then deployed on resource-constrained ZCU104 SoPC hardware platform. Experimental results demonstrate that the optimized lightweight models achieve image classification tasks with over 95% accuracy on SoPC hardware platform. Furthermore, due to minimal impact from quantization errors, the accuracy drop is less than 1%. Compared to GPU (GTX1650), the throughput on SoPC is increased by 96.43%. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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Improved Prototypical Networks for Remote Sensing Scene Classification

Improved Prototypical Networks for Remote Sensing Scene Clas...

引用

2023 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2023

作者： Zhou, Yujin Zhang, Xiang Li, Jie Wang, Guoqing Xie, Yizhuang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

ISBN: (纸本)9781510666351

In practical applications, remote sensing (RS) scene classification faces data shift problems, including novel class and data discrepancy problems. Due to these problems, it is difficult to obtain representative and discriminative features. Therefore, we propose improved prototypical networks (IPN) based on few-shot learning to solve data shift problems in RS scene classification. First, a novel and effective scheme is proposed, which is to introduce Vision Transformer (ViT) pre-trained on a large-scale dataset as the feature extractor of the prototypical networks. Based on the meta-task training framework, IPN can adapt well to RS scene classification tasks and obtain representative features. In addition, a novel loss function called self-distillation-based prototype loss is designed to obtain discriminative features by introducing inter-sample self-distillation and inter-layer self-distillation methods. Extensive experiments are conducted on several public RS scene datasets. Compared with the existing methods, the proposed method achieves an improvement of 4.18%-21.42%. Results demonstrate that the proposed method can effectively solve the data shift problems in RS scene classification. © 2023 SPIE.

关键词： Distillation

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LT-1 Differential Tomography: First Results and Comparison between Monostatic and Bistatic Systems 2

LT-1 Differential Tomography: First Results and Comparison b...

引用

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

作者： Zhou, Shimeng Chen, Zhiyang Li, Yuanhao Hu, Cheng Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China Beijing Institute of Technology Advanced Technology Research Institute Beijing100081 China Beijing Institute of Technology School of Information and Electronic Beijing China

ISBN: (纸本)9798331515669

Differential Tomography SAR technology integrates the advantages of Differential Interferometric SAR and Tomographic SAR, enabling four-dimensional imaging in areas with SAR layover to obtain elevation and deformation information of the target area. Compared to the traditional monostatic mode, the space-based bistatic observation mode has the advantages of interference immunity and richer information acquisition. This paper uses Differential Tomography SAR technology to perform deformation inversion on the LuTan-1 monostatic and space-based bistatic data. While obtaining the elevation information and linear deformation rate of the observed scene, it also compares the results and performance of Differential Tomography under the two different *** final conclusion is that LuTan-1 demonstrates strong capabilities in differential tomography. Additionally, the differential tomography performance of space-based bistatic systems is superior to that of monostatic systems. © 2024 IEEE.

关键词： Miocene

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SSRI-NET: AN ITERATIVE NETWORK FOR SAR SUPER-RESOLTION IMAGING

SSRI-NET: AN ITERATIVE NETWORK FOR SAR SUPER-RESOLTION IMAGI...

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2022 International Conference on Radar Systems, RADAR 2022

作者： Wang, Ziwen Wei, Yangkai Ding, Zegang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9781839537776

SAR imaging is a widely used technique to solve the remote sensing problem. However, the image resolution of traditional imaging methods is limited by signal bandwidth. In order to solve this problem, a new iterative network is proposed to obtain SAR super-resolution images, i.e., SAR Super-Resolution Imaging Network (SSRI-Net). The proposed SSRI-Net is designed to implement SAR super-resolution imaging. In SSRI-Net, the SAR echo is transformed to the SAR reconstruction image, while an additional decoder part does the opposite operation. The SSRI-Net is composed of the unfolded Alternating Direction Method of Multipliers (ADMM), and the decoder part is formulated into a linear mapping to achieve self-supervised imaging. Numerical simulation and real-data experiment in microwave anechoic chamber demonstrate the robustness and effectiveness of the proposed method. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Image resolution

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