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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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research on parallel architecture design of radar real-time signal processing based on CPU-GPU heterogeneous platform 5

Research on parallel architecture design of radar real-time ...

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IET International radar Conference 2020, IET IRC 2020

作者： Dong, Henan Zheng, Cheng Tian, Weiming 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)9781839535635

in view of the central processing unit (CPU) platform processing performance limits on computationally intensive tasks, as well as the FPGA + DSP[1-3] platform of software and hardware of high coupling and difficult defects, using the graphics processor (GPU) powerful parallel computing power, the CPU and GPU pipeline technology, heterogeneous systems with multithreading, GPU more and more flow, such as strategy, puts forward the real-time signal processing system of the multiple perspectives analysis methods, make full use of the independence between modules and signal processing algorithm concurrency features within the *** experimental results show that, compared with the traditional MATlab platform, the maximum acceleration ratio based on CPU-GPU heterogeneous platform can reach more than 20000, and the parallel architecture designed can fully meet the real-time requirements of radar signal processing. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Graphics processing unit

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Distorted SAR target recognition with virtual SVM and AP-HOG feature 5

Distorted SAR target recognition with virtual SVM and AP-HOG...

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IET International radar Conference 2020, IET IRC 2020

作者： Yao, Di Liu, Zhenyuan Li, Feng Li, Yang 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

ISBN: (纸本)9781839535635

Synthetic aperture radar (SAR) plays an indispensable role in national defence and civil fields due to its unique advantages. Although many methods have been proposed, the recognition to SAR images still faces many difficulties, especially to distorted images. This paper first improves the morphological methods attribute profiles (APs) which is widely employed with optical, and hyperspectral images in particular. We combine the APs method with histogram of oriented gradients (HOG) feature (called AP-HOG) to make it suitable for SAR images. Then, the SVM model is used to identify the labelled samples (i.e., Support Vectors) that are closest to the separating hyperplane with maximum margin. We generate different degrees of distorted images based on support vectors, and use the distorted image and the support vector as a new training set for retraining to improve the recognition effect of the distorted image. Finally, we perform experimental verification based on the MSTAR dataset, which proves the effectiveness of the proposed method. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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SAR target recognition using feature extraction of multi-scale monogenic components 5

SAR target recognition using feature extraction of multi-sca...

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IET International radar Conference 2020, IET IRC 2020

作者： Li, Feng Yao, Weijun Li, Yang 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

ISBN: (纸本)9781839535635

In this paper, we combine the monogenic signal with the histogram of oriented gradient (HOG) feature, and propose a new low-level feature: Monogenic-HOG (MG-HOG);then use the bag of words (BoW) model to construct the middle-level feature. Most of the existed features are directly extracted from grayscale synthetic aperture radar (SAR) images. As an extended representation of analytic signals in high-dimensional space, the monogenic signal has excellent characteristics such as rotation and scale invariance in the potential extraction process, and it is also valuable in the image field. However, in the past related work, it is mostly used directly as the feature representation of the image. After experimental validation on the moving and stationary target acquisition and recognition (MSTAR) data set, the proposed feature extraction method has better performance than a number of existed methods. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Feature extraction

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SAR target recognition combining class information and classification hyperplane 5

SAR target recognition combining class information and class...

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IET International radar Conference 2020, IET IRC 2020

ISBN: (纸本)9781839535635

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role. An important step in SAR target recognition is to transform the raw data to a feature space with the property of within-class compactness and between-class separation. However, due to the problem of target aspect angle sensitivity in SAR images, the difference of some targets belonging to the same class in different poses is greater than the difference of targets belonging to different class in similar poses, which often causes misjudgement. To solve this problem, we first use the twin support vector machine (TWSVM) to obtain the classification hyperplane of each class. Then, based on the obtained hyperplane, the projection matrix is constructed by fusing the prior class information of the target. In this way, the sample can be close to the classification hyperplane of the same class and distant from the classification hyperplane of a different class. This algorithm can improve TWSVM's ability to classify SAR images. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The results verify the effectiveness of the proposed algorithm. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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FPGA-Based Implementation of Reconfigurable Floating-Point FIR Digital Filter 8th

FPGA-Based Implementation of Reconfigurable Floating-Point F...

引用

8th International Conference on Communications, Signal processing, and Systems, CSPS 2019

作者： Zhang, Ning Wei, Xin Li, Bingyi Chen, He Radar Research Lab 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 Beijing100081 China

ISBN: (纸本)9789811394089

As a critical digital signal processing method, finite impulse response (FIR) digital filter is widely used in radar signal processing, synthetic aperture radar (SAR) signal processing, etc. Furthermore, an efficient FIR hardware implementation contributes to the practical application of these processing. However, as a computation-intensive operation, the multiple high order FIR digital filter consumes a lot of hardware resources when implemented in commonly used chips such as field-programmable gate array (FPGA). In this paper, a reconfigurable FIR digital filter architecture is presented, which can perform different order FIR filtering operation without FPGA re-programming. In the experiment, the proposed FIR digital filter architecture was implemented and validated on the Xilinx Zedboard Evaluation Kit. The experimental results demonstrate that this design has a low consumption of hardware resources and can achieve real-time processing performance for digital signal processing in the practical applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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FPGA implementation of SAR bi-channel synthesis based on inverse filtering 5

FPGA implementation of SAR bi-channel synthesis based on inv...

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IET International radar Conference 2020, IET IRC 2020

作者： Shao, Xin Shi, Hao Chen, Liang Zhang, Na Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839535635

In a multi-channel synthetic aperture radar (MSAR) system, non-uniform azimuth sampling is an important factor affecting imaging. If it is not processed and then imaged directly, it will inevitably lead to deterioration of imaging performance. The channel synthesis algorithm based on inverse filtering is a method to solve this problem. However, in actual applications, it is necessary to perform specific optimization processing on the algorithm to meet real-time requirements. This paper designs a new architecture for efficient implementation of channel synthesis algorithms. The design makes full use of the parallel characteristics of FPGA to improve the speed of the algorithm. By optimizing the DDR read-write control logic, the data transfer rate is improved. Finally, the Modelsim software is used to obtain the simulation results. At 150 MHz, the processing speed could reach 461.6MSamples/s (64bit/sample). © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Data transfer

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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

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IEEE International Conference on radar

作者： Feng Li Weijun Yao Yang Li Wei Chen Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468893

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance.

关键词： Training Computer vision Image recognition Target recognition Feature extraction Optical imaging radar polarimetry

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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 (IRC 2023)

作者： Changjun Wang Hanpu Zhou Jian Wang Jiake Gao Yunkai Deng Weiming Tian Beijing Building Research Institute Co. Ltd. of CSCEC Beijing 100076 People's Republic of China Radar Research Lab School of lnformation and Electronics Beijing lnstitute of Technology Beijing People's Republic of China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China Chongqing Innovation Center Beijing Institute of Technology People's Republic of China Advanced Technology Research Institute Beijing Institute of Technology Jinan 250300 People's Republic of China

ISBN: (数字)9781837240982

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.

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Lightweight convolutional neural network for false alarm elimination in SAR ship detection 5

Lightweight convolutional neural network for false alarm eli...

引用

IET International radar Conference 2020, IET IRC 2020

作者： Zheng, Xingfei Feng, Yangkai Shi, Hao Zhang, Bocheng Chen, Liang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Shanghai Institute of Satllite Engineering Shanghai China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839535635

SAR ship detection is an important task of SAR image interpretation and plays a significant role in global marine surveillance. However, since there are usually many false alarms in the detection results, a desirable performance is rarely achieved. Aiming at this problem, this paper proposes an improved method for the ship detection process, using the lightweight convolutional neural network EfficientNet-B0 as a false alarm elimination network, cascaded after the detection stage, to reduce the false alarm rate in the detection results and improve the overall detection accuracy. The results on the MSTAR data set show that EfficientNet-B0 has a good classification effect, and the experiments on the custom data set also verify the effectiveness of the network. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Spaceborne Large-Squint Terrain-Matching Synthetic Aperture radar: Concept and technology 6

Spaceborne Large-Squint Terrain-Matching Synthetic Aperture ...

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Wang, Yan Li, Zhe DIng, Zegang Zeng, Tao Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

This paper discusses a new spaceborne large-squint terrain-matching synthetic aperture radar (LSTM-SAR) to effectively image the terrains that do not spread along satellite orbit. Different from the traditional spaceborne SAR, the swath of the LSTM-SAR spreads along the target terrain, instead of the satellite orbit, to perform terrain-matched imaging with both high resolution and short data acquisition period. Aiming at giving a sketch of how the LSTM-SAR works, the paper focuses on discussing the following three main aspects: the data acquisition geometry, azimuth sampling manner and raw data focusing algorithm. Specifically, an optimum squint geometry is firstly suggested for two dimensional swath optimization;then two non-uniform azimuth sampling methods are discussed to avoid data loss induced by transmission blockage;lastly, a wide nonlinear chirp scaling based algorithm is discussed to focus the spatially variant spaceborne LSTM-SAR echo. The presented approach is validated via the computer simulations. © 2019 IEEE.

关键词： Synthetic aperture radar

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