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Modeling and Verification of processing Engines in SAR Imaging SoC

Modeling and Verification of Processing Engines in SAR Imagi...

引用

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

作者： Zhihan Zhang Chao Yang Yizhuang Xie Yongrui Li Ao Zhang Radar Research Lab School of Information and Electoronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the development of real-time requirements for Synthetic Aperture radar (SAR) imaging technology, SAR imaging processing SoC (System on Chip) has become an important research field at present. The subsystem for SAR imaging processing in the SAR imaging SoC needs to be fully verified during the development process. This article designs a UVM-based random verification environment, focusing on the verification requirements of key computing engines and interaction engines in the SAR imaging processing SoC. At the same time, a modeling method for computing engines and interaction engines is proposed, which can quickly and accurately describe the functions and behaviors of processing engines. Collaborating with the verification environment and the reference C model, the design under test achieves satisfactory coverage results. Consequently, the functionality of the processing engines within the subsystem can be comprehensively and effectively verified. The content studied in this article has certain value for the engineering verification implementation of SAR imaging algorithms in the SoC.

关键词： Computational modeling Focusing Streaming media radar polarimetry Data models Real-time systems Timing System-on-chip Engines Synthetic aperture radar

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radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

引用

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

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China Electromagnetic Sensing Research Center of Cemee State Key Laboratory 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 Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB. © 2024 IEEE.

关键词： Clutter (information theory)

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Optimization method of integer pixel motion estimation algorithm based on HEVC

Optimization method of integer pixel motion estimation algor...

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6th International Conference on intelligent Computing and signal processing (ICSP)

作者： Jing-Ping Hong Zhi-Hong Dong Xue Zhang Nan-Nan Song Beijing Key Laboratory of High-end Printing Equipment Signal and Information Processing Beijing Institute of Graphic Communication Beijing China

Test Zone Search (TZSearch) is the default integer pixel search algorithm in motion estimation part of High Efficiency Video Coding(HEVC) reference software HM, which has better performance than Full Search. Nowadays, the demand of high-definition video and real-time video is increasingly intense, but the traditional test zone search algorithm needs to predict multiple initial search points and repeated multi-round diamond search, which leads to high computational complexity and long coding time of test area search algorithm. In this paper, we propose a fast coding method to promote the integer pixel search module for motion estimation in High Efficiency Video Coding. On the premise of statistical characteristics, we optimize the number of search starting points for the algorithm, improve the threshold value for the first diamond search, and finally synchronously set the threshold value in the diamond search part of the raster refinement search. Experimental results show that the TZSearch algorithm reduces the time by 35.03% while ensuring high performance and quality.

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A Wideband Circularly Polarized Folded Transmitarray Antenna With Linearly Polarized Feed 7

A Wideband Circularly Polarized Folded Transmitarray Antenna...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Sun, Xiangdong Guo, Xulong Li, Yin Zhang, Yongliang Li, Yingsong Inner Mongolia University School of Electronic Information Engineering Hohhot010021 China Inner Mongolia University Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot010020 China Peng Cheng Laboratory Shenzhen518000 China Inner Mongolia University School of Transportation Institute Hohhot010021 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350384437

A low-profile, high-gain, wideband circularly polarized folded transmitarray antenna (CPFTA) is proposed, and a polarization-selective linear-to-circular polarization (LP-CP) conversion element is introduced. The CPFTA consists of a linearly polarized (LP) magnetoelectric dipole antenna and a circularly polarized (CP) helical patch antenna. The CPFTA demonstrates excellent performance with a 3dB gain bandwidth of 29.5% and a 3dB axial ratio (AR) bandwidth of 40.9%. © 2024 IEEE.

关键词： Circular polarization

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Fast Algorithm for CU Split in H.266/VVC Intra Based on Texture information

Fast Algorithm for CU Split in H.266/VVC Intra Based on Text...

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intelligent Communications and Computing (ICC), International Conference on

作者： Xue Zhang Zhihong Dong Jingping Hong Peng Cao Beijing Key Laboratory of High-end Printing Equipment Signal and Information Processing Beijing Institute of Graphic Communication Beijing China

In order to improve the coding performance, the H.266/VVC video coding standard adds binary tree split and ternary tree split to complement quadtree split. It also expands the number of angle prediction modes from 33 to 65 in H.265/HEVC, and utilizes new techniques such as wide angle intra prediction. Although the addition of these new technologies improves the encoding performance, it greatly increases the computational complexity of encoding. In this paper, Sobel operator is used to extract the texture information of the coding unit (CU) and exclude the impossible split methods according to the current texture direction of the CU to reduce the computational complexity of the system encoding. The experimental results show that the proposed algorithm can save 15.14% of encoding time compared with the standard model of VTM19.0 while only increasing the Bjontegaard delta bit rate by 0.01%.

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Classification of Bird and Drone Based on radar Time-Frequency Analysis

Classification of Bird and Drone Based on Radar Time-Frequen...

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IET International radar Conference 2023, IRC 2023

作者： Li, Jialin Li, Weidong Wang, Rui Liu, Haibo Wang, Lianjun Zhang, TianRan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Ministry of Education Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

To address the classification of 'Low, Small, Slow' targets such as birds and drones, this paper proposes a classification algorithm based on target time-frequency spectrogram analysis. Firstly, radar-collected data is subjected to Short-Time Fourier Transform (STFT) to obtain target time-frequency spectrograms, followed by data preprocessing. Subsequently, these time-frequency spectrograms are fed into the designed neural network Residual Pyramid pooling Net (RPNet) for the classification of the two target classes. Validation results based on radar experimental data demonstrate that this algorithm can effectively classify birds and drones, thereby improving the classification performance of small targets. © The Institution of Engineering & Technology 2023.

关键词： Drones

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Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model 9th

Novelty Detection-Based Automated Anomaly Identification via...

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9th CCF Conference on Big Data, BigData 2021

作者： Liu, Lianye Liu, Jinping Wu, Juanjuan Zhou, Jiaming Cai, Meiling Hunan Meteorological Science Institute Hunan Changsha410007 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China

ISBN: (纸本)9789811697081

Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training data. As an anomaly detection method, novelty detection only uses normal samples for model learning, which can well fit most of the natural scenes that the amount of abnormal samples is in fact strongly insufficient, such as network intrusion detection, industrial fault detection, and so on, due to the rareness of abnormal events or the high cost of abnormal samples collection. This paper proposes a reconstruction-based ND scheme by introducing an optimized deep generative model (ODGM), which combines the concept of Variational Auto-encoder (VAE) and the generative adversarial network (GAN) model jointly to efficiently and stably learn the essential characteristics from normal samples. A novelty index is established by combining signal reconstruction loss and feature loss between the original signal of the reconstructed signal based on the ODGM on normal samples for anomaly point identification in the test data. The effectiveness and superiority of the proposed model is validated and compared with other representative deep learning-based novelty detection models on two public data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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A SAR Target Recognition Strategy Guided by Electromagnetic Scattering Feature

A SAR Target Recognition Strategy Guided by Electromagnetic ...

引用

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

作者： Yifei Yin Liang Chen Lujiao Liu Yufan Meng Fan Chen Hao Shi Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

SAR target Automatic Target Recognition (ATR) is indispensable in SAR image interpretation. Recently, deep learning technology has been widely used in SAR target recognition tasks. Most networks achieve incremental improvements in target recognition by modifying their structures to extract visual features of targets. However, due to the unique imaging mechanism, relying solely on visual features often leads to the loss of target information. In contrast, the ASC model, which captures the electromagnetic scattering characteristics of the target, plays a crucial role in target recognition tasks. Unfortunately, traditional parameter estimation methods for extracting the ASC model are computationally expensive and time-consuming, making them impractical for real-world applications. To address these issues, we propose a novel target recognition method based on electromagnetic scattering features in this paper. First, a lightweight network-based feature extraction module is designed. Then, the target ASC image is used as the ground truth for guidance, with image intensity and target structure serving as the loss functions during training. Finally, an ASC model-guided feature fusion network is designed, utilizing the fused features for target recognition. On the MSTAR dataset, a visual assessment experiment demonstrated that the proposed feature extraction module effectively extracts electromagnetic scattering features under various operating conditions. Subsequently, in downstream classification tasks, the inclusion of the proposed module resulted in improved accuracy compared to other networks. Additionally, a visualization analysis of the classification network showed that, under the guidance of electromagnetic scattering features, the network achieved good interpretability.

关键词： Training Deep learning Visualization Parameter estimation Target recognition Computational modeling Electromagnetic scattering Imaging Feature extraction radar polarimetry

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Investigation on ULA Fitting Promoting Low Coupling Sparse Arrays

Investigation on ULA Fitting Promoting Low Coupling Sparse A...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Yao, Yichang Shi, Wanlu Li, Yingsong Huang, Zhixiang Yang, Guohui Zhang, Kuang Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150086 China

ISBN: (纸本)9781665452366

A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform subarrays in the basic layer is investigated via parameter estimation like direction-of-arrival(DOA). The results gives a superior performance for the presented LCSA. © 2022 IEEE.

关键词： Low mutual coupling sparse array uniform linear array fitting

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A method for enhancing infrared small target images under complex sky backgrounds

A method for enhancing infrared small target images under co...

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

作者： Yuhan Xu Linbo Tang Haotian Sun Beijing Key Laboratory of Embedded Real-Time Information Processing Radar Research LabSchool of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Infrared target detection is currently widely used in both military and civilian fields. Due to the lower resolution of infrared images compared to visible light images and the lack of texture information in small targets, the quality of infrared small target images is often poor and not suitable for direct detection. Therefore, enhancing low-quality infrared small target images is a necessary step before object detection can be performed. This article presents a method for enhancing infrared images of small targets. The process begins with applying guided filtering to the original image. The resulting detail layer is then used as a guided image for a second guided filtering of the base layer, producing a second detail layer. Additionally, a novel approach to local contrast enhancement is introduced, which applies contrast enhancement specifically to this secondary detail layer. Experimental results has been demonstrated that the enhancement method proposed in this paper significantly improves the visibility of the target area while reducing background interference, without losing information about small targets. Additionally, this method outperforms other common techniques in terms of enhancement effect, as shown in comparative experiments.

关键词： Image quality Image resolution Object detection Interference information filters Data processing Image enhancement

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