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检索条件"机构=Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab"

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SAR image denoising method based on sparse representation

SAR image denoising method based on sparse representation

引用

IET International radar Conference 2018, IRC 2018

作者： Zhou, Hao-Tian Chen, Liang Fu, Bo Shi, Hao Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China 95894 PLA Troops No. 5805 Mail-box Changping District Beijing102211 China

The coherent nature of radar illumination causes the speckle effect, which gives the synthetic aperture radar (SAR) image its noisy appearance. The probability distribution of speckle noise is multiplicative rather than additive, which makes the interpretation and processing of SAR imagery more difficult. A novel SAR image denoising method is proposed. First the multiplicative noise is transformed into additive-like noise by logarithmic transformation. After that, a novel object function is proposed which combines a pre-trained dictionary model to deal with the image. Finally, exponential transform is employed to recover the image. Experimental results show that the proposed method can effectively remove the noise of SAR images, and indicate good performance compared with other state-of-the-art methods. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Additives

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Early Detection of Forest Fire Based on Unmaned Aerial Vehicle Platform

Early Detection of Forest Fire Based on Unmaned Aerial Vehic...

引用

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

作者： Xingsha Yang Linbo Tang Hongshuo Wang Xinxin He Beijing Key Laboratory of Embedded Real-time Information Processing Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

As a kind of natural resources, forest is vital to our survival. Once the fire happening in forest is not found in time, it can cause a huge loss. Accurate real-time monitoring of forest fires is not only an important part of forest fire prevention, but also an important means to effectively control the spread of forest fires and reduce economic losses. Forest areas are often geographically widely distributed and inefficient in manual inspections. Therefore, we need a highly mobile early forest fire warning system to ensure the safety of forest resources. UAVs serve as a highly mobile inspection tool to meet inspection requirements of forest fires. Besides, the early flame characteristics are not obvious, and the single source fire detection is easy to false alarm. In view of this, we propose a forest fire detection method that uses optical images to detect smoke and infrared images to detect fire.

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A tracking filter algorithm for migratory birds based on group situation constraints

A tracking filter algorithm for migratory birds based on gro...

引用

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

作者： Biao Li Rui Wang Jiong Cai The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm.

关键词： Target tracking Heuristic algorithms Dynamics radar Filtering algorithms radar tracking Prediction algorithms Birds information filters radar signal processing

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Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline 2

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

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

作者： Zhou, Hanpu Yan, Kai Wang, Jian Li, Xiaoxiao Tian, Weiming Deng, Yunkai Bejing Institute of Technology Radar Technology Research Institute Beijing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Building Research Institute Corporation Limited Cscec Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798331515669

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data. © 2024 IEEE.

关键词： radar imaging

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An Efficient Transposition Method for Spaceborne SAR Data Based on HBM

An Efficient Transposition Method for Spaceborne SAR Data Ba...

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

作者： Yongrui Li HuShan Lv Ming Xu Liang Chen Zhu Yang Yizhuang Xie Radar Technology Research Institute Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The onboard real-time processing of Spaceborne synthetic aperture radar (SAR) is of great significance for the rapid acquisition of surface information. As a key step of SAR imaging processing, the efficiency of data transposition directly affects the imaging quality and processing speed. In order to solve the problem of traditional double data rate synchronous dynamic random-access memory (DDR SDRAM) limiting the transposition speed, this paper proposes a SAR data transposition method based on high bandwidth memory (HBM). The method makes full use of the high bandwidth and multichannel characteristics of HBM, and significantly improves the transposition speed by optimising the data read/write process, providing a new solution for real-time imaging processing of satellite-borne SAR.

关键词： Limiting Spaceborne radar Imaging Bandwidth radar imaging real-time systems radar polarimetry SDRAM Synthetic aperture radar Surface treatment

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An Enhancing Biological Echo Classification Method with Polarimetric Weather radar Using Random Forest Classifiers

An Enhancing Biological Echo Classification Method with Pola...

引用

IET International radar Conference 2023, IRC 2023

作者： Sun, Zhuoran Wang, Rui Cui, Kai Mao, Huafeng Hu, Cheng Wu, Dongli Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Meteorological Observation Centre of China Meteorological Administration Beijing100081 China

ISBN: (纸本)9781839539954

Polarimetric weather radar can enhance precipitation and biological scatter monitoring by measuring the polarization moments of targets, thus providing valuable shape information. This study employed five Random Forest classifiers to classify birds, insects, and precipitations at the five lowest elevation angles. The training and testing sets were artificially generated using typical cases of birds, insects, and precipitations captured by the polarimetric weather radar. A two-dimensional median filter was employed to reduce the volatility of polarization moments. To mitigate the impact of differential phase jumps, the centre of the probability distribution function for the differential phase in each volume scan was fixed at 180 degrees. The classification accuracy for each elevation angle and sample type exceeded 91%. The performance of the classifiers was assessed using two representative cases. © The Institution of Engineering & technology 2023.

关键词： Distribution functions

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FOUNDATION PIT DEFORMATION MEASUREMENT TECHNIQUE BASED ON GROUND-BASED INTERFEROMETRIC radar

FOUNDATION PIT DEFORMATION MEASUREMENT TECHNIQUE BASED ON GR...

引用

IET International radar Conference 2023, IRC 2023

作者： Wang, Changjun Zhou, Hanpu Wang, Jian Gao, Jiake Deng, Yunkai Tian, Weiming Beijing Building Research Institute Co. Ltd. CSCEC Beijing100076 China Radar Research Lab School of Information and Electronics Bejing Institute of Technology Bejing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

The deformation monitoring data of foundation pit is an important criterion for forecasting and strengthening measures in the field of building safety. During the construction period, the foundation pit will be affected by load, temperature change, random disturbance and other effects to produce deformation. Due to the complex deformation process of foundation pit, it is hard to accurately describe and separate the load trend deformation and elastic thermal deformation by mathematical expression. Excessive thermal deformation may cause cracks in the surrounding pavement and affect the judgment of load deformation. This paper first introduces the deformation measurement method of foundation pit based on differential interferometry technology using ground-based interferometric radar. Based on the obtained deformation type, the simulation verifies the separation effect of wavelet transform on different deformation trends and periodic components. Through the foundation pit deformation monitoring experiment of Tongzhou infrastructure project, the wavelet transform method is used to decompose the foundation pit deformation curve, and the extracted periodic component has good consistency with the temperature information. © The Institution of Engineering & technology 2023.

关键词： Wavelet decomposition

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A Dynamic Short-range Animal Migration Forecast Model Based on Weather radar Network

A Dynamic Short-range Animal Migration Forecast Model Based ...

引用

IET International radar Conference 2023, IRC 2023

作者： Liu, Xuan Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Wu, Dongli Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Meteorological Observation Centre of China Meteorological Administration Beijing100081 China

ISBN: (纸本)9781839539954

Predicting aerial animal migration has great implications for biological research and conservation, and agricultural production. Animal migration is deeply coupled with spatio-temporal and meteorological factors, however, the existing forecast model is only based on the spatio-temporal features of animal migration and the impact of meteorological factors on migration is not considered. In this paper, we propose a dynamic forecast model to forecast airborne migration from the perspective of the influence of wind field data on migration and makes prediction with the help of neural network. Specifically, we calculate the transition probability matrix using the wind data and the migration spatial field to represent the migration dynamic model, and integrated this model into deep neural network. We use the data of China's weather radar network to verify that our model outperforms the competing methods. In addition, the cause of prediction error at peak value is analysed by visualization results. In future applications, more meteorological factors should be considered to improve the representation of dynamic models. © The Institution of Engineering & technology 2023.

关键词： Wind forecasting

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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...

引用

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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Aircraft detection in Remote Sensing image for Space-borne platform

Aircraft detection in Remote Sensing image for Space-borne p...

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

作者： Wei Tang Baozhao Jun Linbo Tang Yu Pan Dongling Jin Zhengpiao Quan Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Aircraft detection in optical remote sensing image is an important research direction in the field of remote sensing. The existing detection methods are difficult to achieve satisfactory results. Traditional detection methods are low robustness, due to manual feature modeling are difficult and subject to background interference; The deep learning target detection method, which improves the detection performance at the cost of complexity improvement, cannot be widely used in space-borne platforms limited resources. In view of the above problems, this paper proposes an aircraft target deep learning detection method of lightweight and multi-scale features. On the basis of the multi-scale target detection framework (SSD), the method firstly uses the dense connection structure and the double convolution channels to form the basic backbone networks with feature reuse and high computational efficiency. To improve the detection performance of the small aircraft target, the basis backbone network connects a residual module and deconvolution to compose the multi-scale feature fusion detection module. Compared with the current classical deep learning object detection methods, the experimental results show that the proposed method has the advantages of maintaining low computational complexity and achieving high detection accuracy.

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