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SAR Tomography with Improved Non-Local Means Filtering Based on Adaptive Window

Journal of Beijing Institute of technology 2023年第6期32卷 670-684页

作者： Shenglei Wang Zhiyang Chen Yuanhao Li Cheng Hu Radar Research Laboratory School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of TechnologyBeijing 100081China

In order to mitigate speckle noise in synthetic aperture radar(SAR)images and enhance the accuracy of SAR tomography,non-local means(NL-means)filtering has been proven to be an effective method for improving the quality of SAR *** from considerations like noise type and the definition of similarity,the size and shape of filtering windows are critical factors influencing the efficacy of NL-means filtering,yet there has been limited research on this *** paper introduces an enhanced NL-means filtering method based on adaptive windows,allowing for the automatic adjustment of filtering window size according to the amplitude information of the SAR ***,a directional window is incorporated to align SAR interferograms,achieving the dual objective of preserving filtering standards and retaining detailed *** results on interferogram filtering and tomography,based on TerraSAR-X data,demonstrate that the proposed method effectively reduces phase noise while maintaining texture accuracy,thereby improving tomography quality.

关键词： NL-means filter adaptive window SAR interferogram filtering SAR tomography

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An autofocusing imaging algorithm for wall penetrating SAR

An autofocusing imaging algorithm for wall penetrating SAR

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IET International Radar Conference 2015

作者： Liu, Wei Zheng, Meng Xiang, Yin Ding, Zegang Zeng, Tao Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology China

ISBN: (纸本)9781849199940

The absence of wall's characteristic will smear and blur the image, shift the target position, and even defocus the imaging result. To solve the problems, a novel autofocusing imaging algorithm is proposed in this paper. Firstly, the electromagnetic field is employed as a 2-D matching filter during the imaging processing. Then, the electromagnetic field in the airto-wall medium is replaced by that in the air-to-air medium. Thirdly, the mismatching error, which is proved to be an extra frequency gap at the shifted depth, is compensated by using the phase cross-correlation method. Finally, the correctness and efficiency of the proposed method are verified by the full wave simulation with data from CST.

关键词： Electromagnetic fields

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real-time Object Detection in UAV Vision based on Neural processing Units 6

Real-Time Object Detection in UAV Vision based on Neural Pro...

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6th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Liu, Ming Tang, Linbo Li, Zongya Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781665431859

With the reduction of Unmanned Aerial Vehicle (UAV) hardware cost and the development of deep learning algorithm, the real-time object detection algorithm applied in UAV vision has great advantages in many fields. However, due to the limited energy consumption and computing power of embedded devices used in the drones and the variable object scales and complex backgrounds in the UAV vision restrict the applications in object detection based on the drones. In this paper, we optimized the generation of anchor boxes, introduced a new module to increase the receptive field to improve the detection of small targets, and used adaptively spatial feature fusion in the feature pyramid to increase feature fusion of multi-scale features. At last we pruned the model to make it lighter and faster, and got the Average Precision (AP) of 89.7% for UAV car aerial images and the speed of 35.7 FPS by running on Neural processing Units (NPUs), which proves the feasibility of the intelligent object detection algorithm's efficient processing in hardware resource limited environment. © 2022 IEEE.

关键词： Energy utilization

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Experimental results and analysis for GBSAR deformation measurement

Experimental results and analysis for GBSAR deformation meas...

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IET International Radar Conference 2015

作者： Zhu, Mao Hu, Cheng Zeng, Tao Deng, Yunkai Tian, Weiming Mao, Cong Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology China

ISBN: (纸本)9781849199940

In this paper, the methodology, which uses GBSAR system to monitor the micro deformation of the scenario, was presented both theoretically and experimentally. In the process, the persistent scatterer (PS) technology was utilized to obtain the deformation of the scene. Firstly, the full basic PS process chain for deformation generation is presented. Secondly, the ground-based SAR system and the data acquisition of geometry are described briefly. Furthermore, the theoretical developments are complemented with the process of real data which is acquired in the experiment. Finally, the comparison between experimental results and local measurements is discussed, which demonstrated the feasibility of our GBSAR system to obtain high-precision deformation.

关键词： Synthetic aperture radar

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Subdomain clutter reduction method for imaging near surface target

Subdomain clutter reduction method for imaging near surface ...

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IET International Radar Conference 2015

作者： Zheng, Meng Liu, Wei Xiang, Yin Ding, Zegang Zeng, Tao Wang, Xun Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology China

ISBN: (纸本)9781849199940

Penetrating radar for detecting near surface target is a kind of radar which can be used to detect, discern and image the small targets buried in the shallow surface of nonmetallic medium. The main clutter of this radar mechanism is the reflection wave transmitted between radar antennas and the surface of the non-metallic medium. If it cannot be reduced, the image of the small targets will be submerged. This paper presents a clutter reduction method which bases on the idea of subdomain mean cancellation to suppress the reflection wave of non-uniform surface of the medium. Experimental results prove that subdomain mean cancellation method can effectively repress the reflection wave of the medium surface and significantly improve the quality of the image.

关键词： Radar antennas

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Implementation of Automatic Face Detection System Based on ARM

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

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

作者： Li, Keng Chen, He 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: (纸本)9781728123455

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. © 2019 IEEE.

关键词： Face recognition

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FPGA-based accelerator for convolution operations

FPGA-based accelerator for convolution operations

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

作者： Cao, Yunfei Wei, Xin Qiao, Tingting Chen, He 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: (纸本)9781728123455

Convolutional neural networks have been widely used in many deep learning applications. Convolutional neural networks have a large number of convolution operations, which poses a huge challenge to real-time performance. In recent years, FPGA implementations of convolutional accelerators have received much attention due to their high performance and energy efficiency. In this paper, we implement an accelerator for convolution operations through the systolic array architecture on Xilinx ZedBoard device. The experimental results show that ours designed accelerators achieving performance density of up to 0.032 Gop/s/DSP. © 2019 IEEE.

关键词： Convolution

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Efficient and Lightweight Target Recognition for High Resolution Spaceborne SAR Images

Efficient and Lightweight Target Recognition for High Resolu...

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

作者： Pan, Yu Tang, Linbo Jing, Donglin Tang, Wei Zhou, Shichao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728123455

Fast and reliable target recognition of the synthetic aperture radar (SAR) images has been widely used in the fields of the marine monitoring, military reconnaissance and strike all over the world. However, due to the difficulty of the intra-class difference and inter-class similarity of the multiclass targets in the high resolution SAR images, the existing methods are difficult to recognize the targets accurately when facing the spaceborne platforms with the high resource constraints. Therefore, in order to solve the above problems, we propose a novel recognition method based on the convolutional neural network (CNN). Firstly, we propose a lightweight CNN framework which regards densely connected convolutional network (DenseNet) as the baseline. Secondly, we advocate a strong discriminative loss function which efficiently improves the recognition accuracy of the targets in the spaceborne SAR images. Experiments are conducted on the TerraSAR dataset and MSTAR dataset to evaluate the proposed method. The results show that our method performs better than the baseline on the both benchmark datasets. © 2019 IEEE.

关键词： Synthetic aperture radar

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Exploring the Potential of Ultra-Low Latency Spiking Neural Network in Remote Sensing Image Classification

Exploring the Potential of Ultra-Low Latency Spiking Neural ...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Jiahao Xu, Ming Chen, He Li, Can Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781839539954

With the rapid advancement of artificial intelligence, the depth of artificial neural networks (ANN) has increased, resulting in higher power consumption and resource utilization. To address these concerns, researchers have begun to explore spiking neural networks (SNN) as an alternative. SNN offers the potential for improved energy efficiency while achieving comparable performance. In recent years, there has been a growing interest in applying SNN to remote sensing classification scenarios. However, one major challenge hindering its widespread adoption is the high latency associated with real-time processing. To address this challenge, we adopt a novel hybrid training approach that combines ANN-SNN conversion with backpropagation through time. We evaluate the effectiveness of our approach on three publicly available datasets: MSTAR, AID and UCMerced. Our experiment results demonstrate that the proposed ultra-low latency SNN achieves classification accuracy rates of 99.05%, 91.36%, and 92.62%, respectively, with a single time step. To the best of our knowledge, this is the first time to apply ultra-low latency SNN to remote sensing image classification tasks. Our findings suggest that this innovative approach has significant potential for future research in the deployment of these models for on-board processing. © The Institution of Engineering & technology 2023.

关键词： Image classification

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Partial Triple Modular Redundancy Method for Fault-Tolerant Circuit based on HITS Algorithm 36

Partial Triple Modular Redundancy Method for Fault-Tolerant ...

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36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Xie, Yu Yu, Wen-Yue Zhang, Ning Chen, He Xie, Yi-Zhuang Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9798350315004

Circuits related to nuclear facilities and on-orbit circuits typically require fault-tolerant reinforcement to cope with harsh radiation environments. While the traditional fully Triple Modular Redundancy (TMR) method can effectively reinforce circuits, it suffers from the problems of large circuit area and high power consumption. To solve this problem, this paper proposes a Partial TMR method based on the HITS algorithm (Hyperlink-Induced Topic Search), which can effectively perform circuit fault tolerance. This method sorts the importance of triggers in the circuit based on the HITS algorithm and uses the Fault Interval time (MTBF) of the Bi-Logic Cone model as the judgment criterion to determine the number of triggers that need to be redundantly designed under partial TMR. Some commonly used modules of an on-orbit processor were tested to verify the effectiveness of the proposed redundancy method in this paper. The experimental results show that the resource overhead can be reduced by at least 20% while achieving an MTBF of 99.9% under TMR. © 2023 IEEE.

关键词： Redundancy

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