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

作者： Zhang, Ning Wei, Xin Chen, Lei Chen, He Radar Research Lab School of Information and Electronics Beijing Institute of Technology China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728123455

Recently, extensive convolutional neural network (CNN)-based methods have been used for real-time remote sensing image processing system. However, the huge storage requirement for model and input image bring a great challenge to hardware implement. In this paper, we propose a three-level memory access architecture for FPGA-based real-time remote sensing image processing system to meet the storage requirement. Moreover, in the proposed architecture, the computational throughput is well matched to the storage bandwidth, which improve the efficiency of arithmetic processing on the hardware platform. In the experiment, we applied the architecture to the hardware implementation of CNN, and implement on Xilinx ZYNQ xc7z035 platform. Our design is evaluated on a main part of the modified YOLOv2 framework [1]. The experimental results demonstrate that this design can access the off-chip memory efficiently, and can provide the required parameters and input data for the CNN on-chip pipeline processing. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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SOPC-based Implementation of Convolutional Neural Network

SOPC-based Implementation of Convolutional Neural Network

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

作者： Feng, Xing Jia, Mingfei Chen, He Radar Research Lab School of Information and Electronics Beijing Institute of Technology China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728123455

Convolutional Neural Network (CNN) is an algorithm widely used in the field of deep learning. Due to the large number of intensive parallel data operations, the use of CPU to implement the CNN serially consumes too much time. In view of the above research background, the System-on-a-Programmable-Chip (SOPC) implementation and acceleration modules of CNN are designed by using the Zynq-7035 development platform launched by Xilinx as the experimental platform. The experiment results show that our method is effective and efficiency for the target image classification on the development platform. © 2019 IEEE.

关键词： Convolutional neural networks

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High Resolution Radar real- time Signal and information processing

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China Communications 2019年第2期16卷 105-133页

作者： Long Teng Zeng Tao Hu Cheng Dong Xichao Chen Liang Liu Quanhua Xie Yizhuang Ding Zegang Li Yang Wang Yanhua Wang Yan School of Information and Electronics Beijing Institute of Technology Beijing 100081 Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing 100081

Radar is an electronic device that uses radio waves to determine the range, angle, or velocity of objects. real-time signal and information processor is an important module for real-time positioning, imaging, detection and recognition of targets. With the development of ultra-wideband technology, synthetic aperture technology, signal and information processing technology, the radar coverage, detection accuracy and resolution have been greatly improved, especially in terms of one-dimensional (1D) high-resolution radar detection, tracking, recognition, and two-dimensional (2D) synthetic aperture radar imaging technology. Meanwhile, for the application of radar detection and remote sensing with high resolution and wide swath, the amount of data has been greatly increased. Therefore, the radar is required to have low-latency and real-time processing capability under the constraints of size, weight and power consumption. This paper systematically introduces the new technology of high resolution radar and real-time signal and information processing. The key problems and solutions are discussed, including the detection and tracking of 1D high-resolution radar, the accurate signal modeling and wide-swath imaging for geosynchronous orbit synthetic aperture radar, and real-time signal and information processing architecture and efficient algorithms. Finally, the latest research progress and representative results are presented, and the development trends are prospected.

关键词： 1D high resolution radar geosynchronous synthetic aperture radar real-time signal and information processing

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Compressive sensing-based super-resolution DOA estimation for mechanical scanning radar

Compressive sensing-based super-resolution DOA estimation fo...

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IET International Radar Conference 2018, IRC 2018

作者： Cheng, Fanglei Wang, Hongyu Li, Yang 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

This study presents a novel super-resolution directions of arrival (DOA) estimation method for mechanical scanning radar by using the advanced compressive sensing algorithm. This method is implemented by constructing a signal model of the mechanical scanning radar through imitating the array radar signal model. Also then it established the compression relationship between the transmitted and received echo signals of the reader by sparsification technique of sending signals. Finally, the DOA estimation can be solved by employing advanced compressive sensing algorithm including basic pursuit (BP), orthogonal matching pursuit (OMP), regularised orthogonal matching pursuit algorithms and so on. Compared with the conventional super-resolution DOA estimation method such as multiple signal classification, the proposed method can distinguish two or more targets within one beamwidth more precisely. Compared with the earlier super-resolution DOA estimation method such as maximum likelihood, the proposed method can obtain more accurate and robust angle difference estimation and do not need to previously know the source number. Both simulated and experimental results show that the overall performance of the proposed DOA estimation is better than that of other methods. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Compressed sensing

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Open set HRRP recognition based on convolutional neural network

Open set HRRP recognition based on convolutional neural netw...

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IET International Radar Conference 2018, IRC 2018

作者： Chen, Wei Wang, Yanhua Song, Jia Li, Yang 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

Most existing algorithms in high-resolution range profile recognition focus on the closed set cases, where the test sample is from a known class. However, a sample could be drawn from unknown classes in realistic scenario, which is named as open set recognition. Here, open set HRRP recognition is achieved by incorporating extreme value theory into convolutional neural network. The softmax layer is replaced by a so-called openmax layer which estimates probabilities of the test sample belonging to known and unknown classes. Experimental results demonstrate that the proposed method outperforms the state-of-art algorithms such as NN, 1-vs-set machine, and W-SVM in terms of correct rejection rate. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Convolution

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Polarimetric radar target recognition framework based on LSTM

Polarimetric radar target recognition framework based on LST...

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IET International Radar Conference 2018, IRC 2018

作者： Chen, Wei Zhang, Liang Song, Jia Wang, Yanhua Li, Yang 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

Polarimetric information is of great importance for radar target recognition. Conventional polarimetric features are hand-designed based on scattering mechanism. In this study, a novel polarimetric target recognition framework based on long-short-term memory (LSTM) network is proposed. The different polarimetric channels are regarded as the sequential inputs in LSTM, and the features are extracted automatically. Experimental results on dual-polarised high-resolution range profile recognition demonstrate that the features learnt by LSTM are more discriminating than conventional features. The recognition performance of the proposed method outperforms the state-of-the-art methods as well. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Long short-term memory

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Feature Enhanced Centernet for Object Detection in Remote Sensing Images

Feature Enhanced Centernet for Object Detection in Remote Se...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tong Zhang Guanqun Wang Yin Zhuang He Chen Hao Shi Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Multi-scale object detection in optical remote sensing imagery is a challenging task due to the varied object scales. Existed state-of-art object detection methods have achieved significant growth. However, most of the methods are based on default anchors, which need to be predefined. The multi-scale object detection accuracy still needs to be improved, especially for small and dense objects. To improve the robustness of the detection algorithm and the performance of multi-scale object detection, a novel anchor-free multi-scale object detection method Feature Enhanced CenterNet is proposed in this paper. First, we use the “encoder-decoder” structure and introduce horizontal connections to enhance feature representation capabilities. Second, an context-aware up-sampling method is proposed to obtain feature maps with suitable scale. To demonstrate the performance of the proposed method, we perform abundant experiments on the public remote sensing datasets. The experimental results demonstrate the robustness and effectiveness of the proposed method.

关键词： Object detection Feature extraction Detectors Robustness Optical imaging Convolution Semantics

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Dynamic mainlobe interference suppression method based on monopulse with gray model Kalman filter

Dynamic mainlobe interference suppression method based on mo...

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IET International Radar Conference 2018, IRC 2018

作者： Feng, Li Zhe, Zhang Di, Yao School of information and electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

The architecture and processing algorithm of mainlobe interference suppression method is described for nulling the signal from mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The existing mainlobe interference suppression method is based on monopulse Wiener filtering. However, the Wiener filtering is more suitable to remove static interference. Therefore, the authors present the dynamic mainlobe interference suppression method with Gray Model Kalman filter to resolve this problem. The simulations verify the proposed method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Kalman filters

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Tracking fluctuating targets in extremely heterogeneous clutter using amplitude information

Tracking fluctuating targets in extremely heterogeneous clut...

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IET International Radar Conference 2018, IRC 2018

作者： Li, Feng Zhuo, Bangsheng Peng, Xiangjun Cheng, Miaomiao Yao, Di School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

This study analyses the radar fluctuating target detection in G0-distributed clutter using dynamic programming - track before detect (DP-TBD) algorithm. G0-distributed clutter is often used to fit the complex circumstances. The authors use chi-square distribution to model the fluctuating targets. The log-likelihood ratio (LLR) is taken as the scoring function of DP-TBD. This study makes contribution to simplify the double integral into single integral in LLR and applies numerical integration with variable resolution to evaluating the LLR. Simulations prove the simplified integral equals the original integral and using LLR in DP-TBD is able to improve the performance of detecting fluctuating targets in G0-distributed clutter. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Dynamic programming

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Ship detection and recognition combing one-dimensional range profile with SAR image

Ship detection and recognition combing one-dimensional range...

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IET International Radar Conference 2018, IRC 2018

作者： Li, Yang Cheng, Miaomiao Peng, Xiangjun Zhuo, Bangsheng Li, Feng School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

Target detection and recognition are two important parts in image processing. As it is known to authors, target detection in synthetic aperture radar (SAR) image is usually processed on two-dimension, which is more complex than one-dimension. Here, the target detection in SAR image is achieved by two levels of one-dimensional target detection. Then, the range profiles of the suspected targets are obtained while the target region is detected. Based on the obtained range profiles, features are extracted and the classifier is used to distinguish the targets and others. Finally, a method of target recognition based on mid-level features is used here. Authors' method is applied on SAR ship data and the result shows that authors' proposed method works well on target detection. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Radar imaging

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