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IET International Radar Conference (IRC 2023)

作者： Xiangdong Tan Xiangguang Leng Jin Wang Kefeng Ji State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

Synthetic Aperture Radar (SAR) is an exceptionally effective imaging technology capable of acquiring ground information regardless of weather conditions and time of day. SAR finds extensive applications in target detection, tracking, and recognition. Despite its wide imaging coverage and high resolution, ship target detection images face several challenges. The most prominent challenge is that both traditional methods such as constant false alarm rate (CFAR) detection and deep neural network methods require complex and time-consuming imaging processing, including but not limited to range migration correction and pulse compression processes. Consequently, real-time detection becomes arduous, leading to delayed results for subsequent applications such as target monitoring and tracking. To address this issue, this study proposes a novel ship detection method that leverages deep learning techniques along with range-compressed data from SAR images to enhance immediate target perception with a convolutional neural network algorithm. The aim is to improve the accurac y and timeliness of ship detection tasks. Given the limited availability of literature on this subject matter, this paper aims to provide an analysis of the proposed method's effectiveness through experimental results while also discussing future prospects.

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Vision Inertial Fusion Based on BP Neural Network for Aircraft Autonomous Positioning

Vision Inertial Fusion Based on BP Neural Network for Aircra...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Liu, Haiqiao Guan, Xujun Luo, Shibin Wu, Xia Xu, Dequan School of Automation Central South University Changsha410083 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Aeronautics and Astronautics Central South University Changsha410083 China

ISBN: (纸本)9789811581540

Long-term navigation for aircraft in global position system (GPS) denied environment is a very challenging task. In this thesis, a novel aircraft autonomous positioning method based on fusion multi-modal image matching and inertial navigation system (INS) is proposed. This method consists of three steps. First, the coordinates of ground control points were obtained by matching the real-time image with the reference image. Then, PnP algorithm is employed for calculating the position of the aircraft. Finally, aircraft gets the optimal position by using BP neural network to combine the position estimated by inertial navigation and the results of multi-modal image matching. Simulation result displays that our method can effectively improve the positioning accuracy of aircraft. The accuracy of this method proposed in this thesis is comparable with that of GPS. Therefore, the study of this data fusion method has certain effects, the positioning accuracy can be improved of the aircraft. It is a great significance for the aircraft to achieve autonomous navigation. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image matching

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Counter-reconnaissance performance analysis of amplitude coded modulation radar waveform

Counter-reconnaissance performance analysis of amplitude cod...

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IET International Radar Conference (IRC 2023)

作者： Zhenyu Liu Xiaobin Liu Qihua Wu Feng Zhao Zhiming Xu Shunping Xiao The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology ChangSha People's Republic of China

ISBN: (数字)9781837240982

The electronic reconnaissance systems can obtain intelligence information from technical parameters of radiation sources. Then, anti-radiation missiles can be used to destroy radiation sources, seriously threatening the survival of radar systems. To improve radar counter-reconnaissance capability, various waveforms are proposed. Among the waveforms, amplitude coded modulation (ACM) can divide the pulse into many short pulses. The signal sorting may fail so that the signal is difficult to be recognized. Therefore, the ACM signal can improve the counter-reconnaissance performance. However, the signal counter- reconnaissance performance has not been discussed. Thus, the uniform and non-periodic ACM model are proposed in this paper. Time-frequency distribution entropy and intercept probability of ACM signal are utilized to evaluate the counter-reconnaissance performance. Simulations are conducted to verify the accuracy of theoretical analysis.

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Time series generator adversarial network with stochastic process for the degradation generation and prediction

Time series generator adversarial network with stochastic pr...

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Data Driven Control and Learning systems (DDCLS)

作者： Anqi Shangguan Nan Feng Lingxia Mu Rong Fei Xinhong Hei Guo Xie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of computer science and Engineering Xi'an University of Technology Xi'an China

Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.

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Characteristics analysis of stepped frequency chirp signal using uniform interrupted modulation

Characteristics analysis of stepped frequency chirp signal u...

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IET International Radar Conference (IRC 2023)

作者： Tiehua Zhao Qihua Wu Zhiming Xu Feng Zhao Shunping Xiao The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

The co-modulation is beneficial for increasing the anti-reconnaissance potential of radar waveform. Stepped frequency chirp (SFC) signal has the characteristics of both linear frequency modulation (LFM) signal and stepped-frequency waveform (SFW). Uniform interrupted modulation (UIM) can increase the complexity of the signal in the time domain. Therefore, the characteristics of the SFC signal with UIM are deduced in this paper. First of all, the processing method of the SFC signal is introduced. Then the characteristics of the SFC signal with UIM are analysed emphatically. The high-resolution range profile (HRRP) of the inter-pulse and intra-pulse interrupted modulation are both deduced respectively. It is concluded that the UIM between pulses brings the HRRP of the SFC signal multiple pseudo peaks extending along the range profile. However, there is no pseudo peak in the HRRP of the intra-pulse interruptedly modulated SFC signal. Signal simulation experiments are carried out with MATLAB to verify the accuracy of the theoretical analysis.

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance 5

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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5th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Chen, Maolin Chen, Songhang Lian, Mingchang School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9798331507992

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs. © 2024 IEEE.

关键词： Automatic guided vehicles

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Anechoic chamber equivalent simulation method for intermittent transceiver echo based on space target electromagnetic scattering data

Anechoic chamber equivalent simulation method for intermitte...

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IET International Radar Conference (IRC 2023)

作者： Zhenyu Qiao Xiaobin Liu Ailun Xie Qihua Wu Feng Zhao Ling Wang The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

Electromagnetic simulations and anechoic chamber measurements are commonly used to obtain target characteristics. The use of intermittent transceiver in anechoic chamber can effectively solve the problem of the signal coupling at the receiver. However, the method of equivalent simulation between electromagnetic simulation and intermittent transceiver is rarely discussed. In this paper, an intermittent transceiver echo equivalent simulation method in anechoic chamber is proposed based on space electromagnetic scattering data. Then, the characteristics of scattering points of targets under micro-motion conditions are analyzed. Finally, the equivalent relationship and processing flow between electromagnetic scattering data and intermittent transceiver echo are discussed. Simulations are conducted to verify the effectiveness of the proposed method.

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guo Mao Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex System National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data.

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An Optimized One-Stage Detector for Autonomous PV Defect Detection

An Optimized One-Stage Detector for Autonomous PV Defect Det...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Huidi, Xiao Jiajia, Zhao Lihui, Wang Qiucheng, Shen Nongjie, Ma Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing210096 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789811694912

To realize the defect detection of Photovoltaic (PV) modules based on infrared images, a one-stage detector based on FPT and loss function optimization is proposed. Firstly, ResNet50 is selected as the backbone network for feature extraction, combining with the idea of migration learning to strengthen its feature extraction ability. Besides, replacing the traditional feature pyramid with FPT, using Transformer structure help realize the interaction of the same position between different pyramid layers as well as different positions of the same layer, improving the feature fusion capability of the model. Finally, to alleviate the problem of suppression of non-optimal predictions by NMS strategy, a bipartite graph matching algorithm based on loss function minimization is designed to enhance the learning of the distribution of ground truth targets by the network. The dataset is expanded by Mosaic data enhancement method for model training, so as to achieve infrared image-based PV defect detection. Results show that the improved one-stage target detection network has a promising detection effect on PV module defects under infrared images, with the average accuracy improving by 2.20% compared with the original algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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Depth estimation using an improved stereo network

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Frontiers of Information technology & Electronic Engineering 2022年第5期23卷 777-789页

作者： Wanpeng XU Ling ZOU Lingda WU Yue QI Zhaoyong QIAN Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering UniversityBeijing 101416China Peng Cheng Laboratory Shenzhen 518055China Digital Media School Beijing Film AcademyBeijing 100088China

Self-supervised depth estimation approaches present excellent results that are comparable to those of the fully supervised approaches,by employing view synthesis between the target and reference images in the training ***,which serves as a backbone network,has some structural deficiencies when applied to downstream fields,because its original purpose was to cope with classification *** low-texture area also deteriorates the *** address these problems,we propose a set of improvements that lead to superior ***,we boost the information flow in the network and improve the ability to learn spatial structures by improving the network ***,we use a binary mask to remove the pixels in low-texture areas between the target and reference images to more accurately reconstruct the ***,we input the target and reference images randomly to expand the dataset and pre-train it on ImageNet,so that the model obtains a favorable general feature *** demonstrate state-of-the-art performance on an Eigen split of the KITTI driving dataset using stereo pairs.

关键词： Monocular depth estimation Self-supervised Image reconstruction

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