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Research on Dataset Preparation and Registration Methods for Multi-Temporal Airport SAR Images 2

Research on Dataset Preparation and Registration Methods for...

引用

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

作者： Wu, Guanghui Liu, Lujiao Li, Jianhao Pan, Hongxin Shi, Hao Beijing Institute of Technology Chongqing Innovation Center Chongqing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture radar (SAR) offers high-resolution imaging capabilities that are unaffected by weather and lighting conditions, making it ideal for complex environments. Recent advancements in spaceborne SAR technology have provided diverse high-resolution, multi-temporal SAR data, crucial for applications like airport monitoring. Despite these advancements, there is a notable lack of multi-temporal airport SAR datasets for research. This study addresses this gap by presenting a method to create a multi-temporal airport SAR image dataset. Utilizing known latitude and longitude information, the method converts geographic coordinates to image projection coordinates to locate airports in SAR images, followed by image conversion and alignment to construct the dataset. Additionally, this study introduces a registration method named SAR-SURF for multi-temporal SAR images, employing a SURF-based Feature Points Detector and an improved Fast Sample Consensus (FSC) algorithm. The experimental results demonstrate that this method effectively reduces speckle noise, ensures matching accuracy, and minimizes processing time, enabling rapid and precise registration of multi-temporal airport SAR images. © 2024 IEEE.

关键词： Image registration

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Vague Prototype-Oriented Diffusion Model for Multi-class Anomaly Detection 41

Vague Prototype-Oriented Diffusion Model for Multi-class Ano...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Yuxin Feng, Yaoxuan Chen, Bo Chen, Wenchao Wang, Yubiao Hu, Xinyue Sun, Baoling Qu, Chunhui Zhou, Mingyuan National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Xidian University Xi'an710071 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China McCombs School of Business The University of Texas at Austin AustinTX78712 United States

Multi-class unsupervised anomaly detection aims to create a unified model for identifying anomalies in objects from multiple classes when only normal data is available. In such a challenging setting, widely used reconstruction-based networks persistently grapple with the "identical shortcut" problem, wherein the infiltration of abnormal information from the condition biases the output towards an anomalous distribution. In response to this critical challenge, we introduce a Vague Prototype-Oriented Diffusion Model (VPDM) that extracts only fundamental information from the condition to prevent the occurrence of the "identical shortcut" problem from the input layer. This model leverages prototypes that contain only vague information about the target as the initial condition. Subsequently, a novel conditional diffusion model is introduced to incrementally enhance details based on vague conditions. Finally, a Vague Prototype-Oriented Optimal Transport (VPOT) method is proposed to provide more accurate information about conditions. All these components are seamlessly integrated into a unified optimization objective. The effectiveness of our approach is demonstrated across diverse datasets, including the MVTec, VisA, and MPDD benchmarks, achieving state-of-the-art results. Copyright 2024 by the author(s)

关键词： Normal distribution

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A Relay Buffer Prediction Method Based on LSTM in Terminal Cooperative Communication 23

A Relay Buffer Prediction Method Based on LSTM in Terminal C...

引用

23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Fei, Jie Zhi, Hui Tie, Kui Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electronic and Information Engineering Anhui University Hefei China CO. Ltd Bengbu China

ISBN: (纸本)9798350325959

In terminal cooperative communication, relay assisted communication with buffer has significant advantages in improving system throughput, signal-to-noise ratio, and reducing system outage probability. The existing relay methods with buffer are all fixed buffer method with a fixed size of the buffer and only considering relay wholehearted cooperation and forwarding, that is, all buffer of the relay assists communication. However, when the forwarding task is small and there is a large idle in forwarding buffer, the buffer area cannot be fully utilized. When the forwarding task is large and forwarding buffer is busy, packet loss may occur in the forwarding task. In order to improve the usage efficiency of relay buffer and reduce packet loss, this paper proposes a relay buffer prediction method based on the long short-term memory network to predict the amount of data that the relay needs to forward. According to the prediction, the size of the relay buffer is divided, so that the buffer can be dynamically adjusted on demand. Simulation results show that, compared with the fixed buffer method, the relay buffer prediction method has a better buffer usage efficiency and a lower packet loss rate. © 2023 IEEE.

关键词： Buffer Forecast LSTM Relay

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

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

引用

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Direction-of-arrival estimation Sparse approximation Computational modeling Superresolution Estimation signal processing algorithms Data models Convolutional neural networks Computational complexity signal to noise ratio

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Feature Attention Complex-Valued-Based CNN for Ship Target Recognition of SAR Images 2

Feature Attention Complex-Valued-Based CNN for Ship Target R...

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

作者： Pan, Hongxin Yang, Chao Yin, Yifei Liang, Bin Shi, Hao Wu, Guanghui Beijing Institute of Technology Chongqing Innovation Center Chongqing China Shanghai Institute of Satellite Engineering Shanghai China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

The details regarding targets within Synthetic Aperture radar (SAR) imagery are typically conveyed and maintained in complex-valued form. Namely, the information regarding both the amplitude and phase is essential. In the process of recognizing targets with SAR, the network only utilizes the amplitude image as input, while the phase information is neglected, resulting in poor recognition performance. This paper proposes a Feature Attention Complex-Valued-Based convolutional neural Network (FACV-Net) for SAR ship recognition to overcome these issues, which effectively utilizes phase information to improve target recognition. Firstly, to meet the requirements of network training in complex domains, a series of complex-valued operation blocks have been constructed. Moreover, a new complex-valued Attention Module (CAM) is introduced to ensure that the network focuses on the amplitude and phase characteristics of the target separately. Additionally, to address the mismatch problem between the amplitude and phase data, two distinct methods for complex-valued max-pooling are utilized. Ultimately, the performance of the proposed FACV-Net is assessed using the OpenSARship dataset, and the results indicate that this novel approach surpasses conventional networks in terms of recognition accuracy. When the CAM is added, the performance of the network sees a further enhancement of around 3% in accuracy. These findings confirm the proposed method's effectiveness and advantage over others. © 2024 IEEE.

关键词： Cams

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Calibration and Estimation for FDA-MIMO radar with Random Amplitude and Phase Errors

Calibration and Estimation for FDA-MIMO Radar with Random Am...

引用

2023 IEEE International radar Conference, radar 2023

作者： Liu, Feilong Zhu, Shengqi Xu, Jingwei Li, Ximin Xu, Yanhong Si, Wenwen Xidian University National Key Laboratory of Radar Signal Processing Shannxi Xi'an China Xi'an University of Science and Technology The School of Communication and Information Engineering Shannxi Xi'an China Beijing Institute of Space Long March Vehicle Beijing Xi'an China

ISBN: (纸本)9781665482783

The combination of frequency diverse array (FDA) with range-angle-time dependent beampattern and waveform diversity in multiple input multiple output (MIMO) radar, namely, FDA-MIMO radar, offers additional degrees-of-freedom to enhance the overall system performance. The existing parameter estimation algorithms for FDA-MIMO radar are difficult to achieve effective target estimation when there is amplitude-phase error in the array antenna. In this paper, the proposed method can achieve the calibration and estimation for FDA-MIMO radar with random amplitude and phase errors. First, the signal model for FDA-MIMO radar signal model with random amplitude and phase errors is established. Second, the angle and range estimation is obtained by the spectral peak search method with reduced dimension. Then, the amplitude and phase error of each array element are estimated by the singular value decomposition and eigenvalue decomposition. Finally, the multiple simulation experiments show that in the presence of amplitude and phase errors, the existing algorithms have completely failed, but the proposed algorithm still has the ability to estimate the target with high precision. © 2023 IEEE.

关键词： Singular value decomposition

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Efficient Joint Deployment of Multi-UAVs for Target Tracking 19th

Efficient Joint Deployment of Multi-UAVs for Target Tracki...

引用

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Jiashuai Sun, Lu Wan, Liangtian Zheng, Jibin Wang, Xianpeng Institute of Information Science Technology Dalian Maritime University Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China National Laboratory of Radar Signal Processing Xidian University Xian China State Key Laboratory of Marine Resource Utilization in South China Sea and College of Information Science and Technology Hainan University Haikou China

ISBN: (纸本)9783031651229

Target tracking plays an important role in many real-world applications, such as in vegetation protection, disaster rescue, wildlife observation, etc. Multiple unmanned aerial vehicles (multi-UAVs) can provide effective services for target tracking through efficient joint deployment satisfying the system constraints. The constraints include the energy and no-fly zone constraint of each UAV, the communication distance and safe distance among multi-UAVs, the number of UAVs. As there exists no ready-made model that considers all the above constraints, meanwhile, the energy and no-fly zone restrict the deployment of multi-UAVs for target tracking. In this paper, we propose a model which considers no-fly zone and the energy constraint simultaneously for the joint deployment of multi-UAVs. The objective function is defined as minimizing the distance of each UAV and the threats of target so that it can deploy multi-UAVs reasonably. Second, we propose an improved Nondominated Sort Genetic Algorithm II (NSGAII) with new encoding and decoding mechanisms and the no-fly zone avoidance strategy which can improve the searching performance of joint deployment of multi-UAVs. Finally, we design a set of experiments and the experimental results demonstrate that our proposed algorithm can deploy multi-UAVs for target tracking efficiently compared with the state-of-the-arts methods. © ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Unmanned aerial vehicles (UAV)

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Real-time Implementation of Phased Array radar Tracking Algorithm Based on Embedded GPU 2

Real-time Implementation of Phased Array Radar Tracking Algo...

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

作者： Fang, Delin Lin, Ruiqi Chang, Ningyuan Chen, Youwang Tian, Weiming Deng, Yunkai Beijing Institute of Technology Radar Technology Research Institute Beijing China Beijing Key Laboratory of Embedded Realtime Information Processing Technology Radar Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

The embedded Graphics processing Unit (GPU) module, which includes both Central processing Unit (CPU) and GPU processors, can be easily integrated into radar systems, offering high performance and flexibility. Phased array radar tracking algorithm involves numerous modules with poor parallelism between them, leading to significant data blocking issues during sequential execution. Traditional implementations using CPUs or DSPs are time-consuming and inefficient. To address this problem, this paper proposes a real-time implementation solution based on embedded GPU. The solution decouples logic-intensive and computation-intensive tasks in the algorithm and maps them separately to the CPU and GPU within the embedded GPU module. Then three-level hybrid parallel processing method is adopted, which is based on grid-stride-based intra-kernel thread parallelism, CUDA stream asynchronous parallelism and CPU thread parallelism in order to realize real-time processing of radar echo data. Verification experiment using real measured data indicates that the proposed real-time implementation solution achieves significant acceleration with no loss of accuracy on the Jetson AGX Xavier embedded GPU platform, markedly improving tracking efficiency. © 2024 IEEE.

关键词： Digital storage

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HIGH-PRECISION DSM-BASED ALGORITHM FOR SAR ABSOLUTE RADIATION CALIBRATION

HIGH-PRECISION DSM-BASED ALGORITHM FOR SAR ABSOLUTE RADIATIO...

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IET International radar Conference 2023, IRC 2023

作者： Tang, Fanyi Wang, Junrui Wang, Zhibin Lou, Jiahan Guo, Huancheng Li, Zhenfang National Key Laboratory of Radar Signal Processing Xidian University Xi'an China ShangHai Institute of Aerospace Electronics Technology Shanghai China Country Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9781839539954

Radiation calibration is crucial for synthetic aperture radar (SAR) images, but it faces limitations due to the limited availability of corner reflectors. In this paper, we introduce an innovative algorithm to determine the absolute radiation calibration coefficient for SAR images using high-precision Digital Surface Model (DSM). This algorithm adapts to more common observation scenarios, reducing its dependence on reflectors. Firstly, the DSM is employed for target geolocation, providing precise local incidence angle information closely tied to the backscatter coefficient. Secondly, specific land cover types (such as forests, bare soil, or water bodies) are estimated based on a priori scattering model and local incidence angle. Finally, the absolute radiometric calibration coefficient is calculated by comparing observed and theoretical backscatter coefficients. This study utilized the DSM product and SAR image from China's first interferometric spaceborne synthetic aperture radar (InSAR) system, "Tianhui-2," to validate the algorithm's accuracy. Experimental results demonstrate an estimation accuracy exceeding 0.3 dB for the absolute radiation calibration coefficient. © The Institution of Engineering & Technology 2023.

关键词： Backscattering

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GEOSYNCHRONOUS SAR 3-D TARGET RECONSTRUCTION BASED ON MULTI-ANGLE OBSERVATION

GEOSYNCHRONOUS SAR 3-D TARGET RECONSTRUCTION BASED ON MULTI-...

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IET International radar Conference 2023, IRC 2023

作者： Zhu, Kaiwen Zhang, Tianyi Li, Linghao Ding, Zegang Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839539954

Geosynchronous synthetic aperture radar (GEO SAR) has the ability of multi-angle observation, and the potential to reconstruct the three-dimensional (3-D) structure of target. To fully utilize this ability, a 3-D target reconstruction method based on multi-angle observation and forward projection is proposed. The core idea of the proposed method is to determine whether target exists on a 3-D position by forward projecting the 3-D position onto the multi-angle SAR images. The proposed method is well validated via simulation and real data experiment. © The Institution of Engineering & Technology 2023.

关键词： 3D reconstruction

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