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检索条件"机构=Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology"

共 576 条记录，以下是281-290 订阅

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Robust space-time adaptive processing based on covariance matrix reconstruction and steering vector correction

Robust space-time adaptive processing based on covariance ma...

引用

IET International Radar Conference 2018, IRC 2018

作者： Hu, Xueyao Zhang, Xinyu Li, Yang Wang, Hongyu Wang, Yanhua Beijing Key Laboratory of Embedded Real-Time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China College of Electronic Sciences National University of Defence Technology Changsha410073 China

Clutter presents considerable heterogeneity in forward-looking airborne radar (FLAR) applications and conventional space-time adaptive processing (STAP) methods are sensitive to model mismatch. As a result, when a strong target signal contaminates the training samples, despite the use of guard cells, the performance of conventional STAP methods degrades significantly. In this study, a robust method, which involves reconstructing a target-free covariance matrix and correcting the presumed steering vector to prevent target cancellation in FLAR, is proposed. First, the target-free covariance matrix is reconstructed through integrating the spatial-temporal spectrum over a sector separated from the desired frequency and direction of targets. Subsequently, the mismatch between presumed steering vector and actual steering vector is corrected via quadratic optimisation. In addition, the processing scheme is applied to real-measured clutter data, and the experimental results validate the effectiveness of the proposed method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Space time adaptive processing

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Algorithm Implementation of On-Board SAR Imaging on FPGA+DSP Platform

Algorithm Implementation of On-Board SAR Imaging on FPGA+DSP...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Yu, Wenyue Xie, Yizhuang Lu, Dan Li, Bingyi Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai201109 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9781728123455

This paper introduces an effective parallel processing method to design the on-board SAR (Synthetic Aperture Radar) real time imaging processor using FPGA+DSP based on the high-resolution imaging algorithm. The architecture of this processor is designed based on the analysis of the algorithm operation characteristics and the inherent time relationship. In order to reduce the time consumption, pipeline and parallel joint processing method is applied. In addition, the system uses a combination of floating-point operations and fixed-point operations, which not only meets the imaging accuracy requirements but also saves the hardware scale of the system. The system requires 24s to focus the GF-3 stripmap SAR raw data with a granularity of 1638416384 when works in 100MHz. The results demonstrate that our method was effective and the imaging quality can meet the requirements. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Space Target Super-resolution Based on Low-complex Convolutional Networks

Space Target Super-resolution Based on Low-complex Convoluti...

引用

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

作者： Tingting Cui Linbo Tang Jinghong Nan Zhenzhen Li Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The images super-resolution is a key technique for space target, which has important research significance and application value. However, the traditional super-resolution algorithms always fail to achieve a good reconstruction effect due to limited information and inaccurate manual design models. Moreover, deep learning algorithms mainly focus on improving reconstruction with the price of high complexity. To address the above issues, we propose an improved space target super-resolution based on low-complex convolutional neural networks. Specifically, we construct a three-layer convolutional neural network by changing network layers and convolution kernels, which reduces the number of parameters and improves the extraction characteristics and reconstruction. Meanwhile, we establish a complete training data set, which solves the problem that the space target images are difficult to obtain. The experimental results demonstrate that the algorithm proposed can achieve the space target super-resolution. Compared with other algorithms, our algorithm obtains better subjective visual effects and objective evaluation indicators.

关键词： super-resolution deep learning convolutional neural networks

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SAR Parameter Estimation Method for Rectangle Plane Based on information Geometry

SAR Parameter Estimation Method for Rectangle Plane Based on...

引用

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

作者： Yuhan Wen Xinliang Chen Yangkai Wei Yujie Fan Tao Zeng Zegang Ding Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

After establishing scattering models, the traditional synthetic aperture radar (SAR) parameter estimation methods usually utilize vanilla gradients to estimate target parameters from SAR echoes or images. However, the vanilla gradients will lead to lower iteration efficiency. Hence, in this paper, to take the rectangle plane as an example, we propose a SAR parameter estimation method based on the information geometry (SPEM-IG) to solve the above problem. The SPEM-IG adopts the natural gradient to estimate the parameters based on the information geometry optimization algorithm. The central objective of SPEM-IG is to project the least square function into probability distribution space and find the optimal solution in the new parameter space. In order to reduce the computational complexity caused by the multi-peak feature of least squares function, SPEM-IG utilizes the range of parameters rather than its exact value in calculating the value of least squares function, and takes the optimal solution in this range as the result of the current iteration process. By considering the geometric structure of manifolds, SPEM-IG calculates the optimal gradient direction called natural gradient to obtain faster convergence rate. The experimental results show that the SPEM-IG is effective for estimating target parameters. Besides, SPEM-IG has good performance even at low signal-to-noise ratios of SAR images.

关键词： parametric scattering model information geometry natural gradient

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Research on Infrared And Vsisible Image Fusion System

Research on Infrared And Vsisible Image Fusion System

引用

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

作者： Xinxin He Linbo Tang Chen Wu Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Infrared and visible image fusion is an important branch of image fusion technology. By combining the image information of two different bands, the human eye or machine can detect the target more comprehensively, quickly and accurately, and image fusion can improve the ability of human vision to understand the scene, which has great application value in military and civilian fields. Therefore, the research of infrared and visible image fusion systems has important theoretical and practical significance. In this paper, the author takes infrared and visible images as the research object, expounds the basic theory of image fusion, designs the fusion algorithm and the system based on FPGA + ARM, and also gives the complete solution of image fusion system. The experimental results show that the image fusion system is built successfully, the system image fusion effect is good, and the real-time processing is realized. After further experiment and design optimization, the system can be applied in engineering.

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Radar HRRP recognition based on CNN

Radar HRRP recognition based on CNN

引用

IET International Radar Conference 2018, IRC 2018

作者： Song, Jia Wang, Yanhua Chen, Wei Li, Yang Wang, Junfu 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 Racobit Electronic Information Technology Co. Ltd. Beijing China

In this study, ground target recognition based on one-dimensional convolutional neural network (CNN) is studied by exploiting the targets' high-resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially, CNN can automatically discover features for classification. The authors propose a multi-channel CNN architecture that can be applied on diverse forms of HRRP such as amplitude, complex, spectrum etc. Experimental results demonstrate the superiorities of the proposed method over conventional methods based on handcrafted features and single-channel CNN in terms of recognition accuracy. Visualisation of the 'deep features' shows higher separability than handcrafted features, thus providing an insight into its effectiveness in exploiting the intrinsic structures. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Radar target recognition

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Cascaded Kalman filter for target tracking in automotive radar

Cascaded Kalman filter for target tracking in automotive rad...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, Yang Liang, Can Lu, Man Hu, Xueyao Wang, Yanhua 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 Racobit Electronic Information Technology Co. Ltd. Beijing China

In automotive anti-collision radar system, the accuracy of velocity is one of the key indicators to measure radar performance. For frequency modulation continuous wave radar with chirp sequence, which is commonly used in automotive radar, there are two problems that are velocity ambiguity and measurement error. In order to address these problems, a cascaded Kalman filter algorithm is proposed in this article, which is able to overcome velocity ambiguity and improve the accuracy of measurements. In addition, the problem of velocity ambiguity can be solved just in the data processing and it avoids transmitting staggered or multiple pulse repetition frequency signal. The simulation results show that this algorithm can effectively improve the tracking performance. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Data handling

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Compressive Multidimensional Harmonic Retrieval with Prior Knowledge

Compressive Multidimensional Harmonic Retrieval with Prior K...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Li, Yinchuan Zhang, Xu DIng, Zegang Wang, Xiaodong School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Electrical Engineering Department Columbia University New York United States Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781728123455

This paper concerns the problem of estimating multidimensional (MD) frequencies using prior knowledge of the signal spectral sparsity from partial time samples. In many applications, such as radar, wireless communications, and super-resolution imaging, some structural information about the signal spectrum might be known beforehand. Suppose that the frequencies lie in given intervals, the goal is to improve the frequency estimation performance by using the prior information. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to given intervals. We then propose to solve the frequency-selective atomic norm minimization by converting them into semidefinite program based on the MD Vandermonde decomposition. Numerical simulation results are presented to illustrate the good performance of the proposed method. © 2019 IEEE.

关键词： Frequency estimation

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Airfield detection based on JPEG2000 compressed domain

Airfield detection based on JPEG2000 compressed domain

引用

IET International Radar Conference 2018, IRC 2018

作者： Duan, Chenhui Zhao, Baojun Tang, Linbo Li, Cheng Li, Chen 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 Information Security Laboratory School of Information North China University of Technology Beijing100093 China

With the rapid increase of optical remote sensing (RS) images, on-orbit specific target detection has been facing more and more challenges: limited processing environment, wireless bandwidth, space, power and storage. To resolve this issue, visual characteristics in the compressed domain are exploited to represent and recognise specific objects. In this study, a new saliency method and an airfield detection framework in the JPEG2000 compressed domain is proposed. First, the authors exploit the header information in raw JPEG2000 code streams to generate structural saliency map, and a great deal of nonstructural regions in RS images are eliminated. Second, airfield candidates are extracted from low frequency wavelet sub-bands using line segment detection and the parallel density model. Finally, the geometrical properties of airfield candidates are represented by the speed-up robust features descriptor, and the support vector machine classifier is utilised to gain the final detection results. The proposed framework is evaluated on self-made dataset. Compared with the existing relevant state-of-the-art approaches, the proposed method reduces the processing time by 60% while guaranteeing the detection rate. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Optical remote sensing

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Implementation of Automatic Face Detection System Based on ARM

Implementation of Automatic Face Detection System Based on A...

引用

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

作者： Keng Li He Chen Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The embedded automatic face detection system is to locate the face region in the real scene. In order to reach a real-time performance, We adopt a cascaded structure of the convolution neural network model to solve face detection problem. In hardware implementation, the ARM Cortex-A9 processor is used to achieve the algorithm. And the Neon unit in ARM is used to achieve the parallel operation of the basic data of the algorithm, so the detection algorithm is accelerated to achieve the real-time operation of the automatic face detection system.

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