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A Survey of Underwater Multi-Robot systems

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 1-18页

作者： Ziye Zhou Jincun Liu Junzhi Yu State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and RoboticsBeijing Innovation Center for Engineering Science and Advanced TechnologyCollege of EngineeringPeking UniversityBeijing 100871China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent *** this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new *** specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their ***,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of *** conclude,we have discussed related promising directions for future *** survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

关键词： Cooperation formation control multi-robot systems(MRS) taxonomy underwater robots underwater tasks

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Radar Signal Detection Based on Ambiguity Function and Deep Learning 2

Radar Signal Detection Based on Ambiguity Function and Deep ...

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2nd International Conference on Computer Engineering and Application, ICCEA 2021

作者： Lv, Fengrong Dong, Zeyu Wan, Tao Jiang, Kaili Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665426169

The ambiguity function describes the joint characteristics of the signal in the time domain and the frequency domain, and is usually used for signal recognition. The time domain and frequency domain distribution of noise is more spurious than the signal. Its ambiguity function can also reflect its characteristics. However, the direct use of the ambiguity function as the signal feature will increase the amount of calculation of the detection system, so slice it, which reduces the burden on the system for detecting signal information while preserving the information. At the same time, the radar signal detection based on neural network can avoid the influence of artificially setting the threshold on the detection performance of the system. So, we propose an algorithm for inputting the extracted fuzzy function slice sequence into a neural network to realize signal detection. simulation proves that when the signal-To-noise ratio of the received signal is very low, our proposed method still has a high detection probability. © 2021 IEEE.

关键词： Signal detection

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Adaptive watermark generation mechanism based on time series prediction for stream processing

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Frontiers of Computer science 2021年第6期15卷 59-73页

作者： Yang SONG Yunchun LI Hailong YANG Jun XU Zerong LUAN Wei LI Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China College of Life Sciences and Bioengineering Beijing University of TechnologyBeijing 100083China

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in *** reality,streaming data usually arrives out-of-order due to factors such as network *** data stream processing framework commonly adopts the watermark mechanism to address the data *** is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be *** watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and *** paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above *** mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result *** implement the proposed mechanism on top of Flink and evaluate it with realworld *** experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

关键词： data stream processing watermark time series based prediction dynamic adjustment

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Radar Signal Modulation Recognition Based on Bispectrum Features and Deep learning 2

Radar Signal Modulation Recognition Based on Bispectrum Feat...

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2nd International Conference on Computer Engineering and Application, ICCEA 2021

作者： Dong, Zeyu Lv, Fengrong Wan, Tao Jiang, Kaili Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665426169

Signal bispectral transformation can not only suppress the influence of Gaussian white noise on signal modulation recognition, but also retain the signal amplitude and phase information. It is also used to extract the non-linear characteristics. Compared with other high-order spectra, bispectrum has a simple processing flow. However, the direct use of all bispectrum as signal features will lead to two-dimensional template matching, causing lots of calculations. Converting two-dimensional bispectrum into one-dimensional sequence, for example, extracting slice information of bispectrum, or using integral bispectrum apparently reduce the amount of data to be processed while retaining part of the bispectrum information. We input the extracted bispectral transformation of radar signals into the neural network to realize modulation recognition. The simulations validate our conclusions that our proposed methods still have a high recognition probability while SNR is low. © 2021 IEEE.

关键词： Template matching

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Semi-supervised Open-set Recognition of Radar Active Jamming

Semi-supervised Open-set Recognition of Radar Active Jamming

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Hao Fang, Xueli Zhang, Lei Kang, Hui Zhang, Wei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665498142

With the emergence of various kinds of complex radar jamming, radar jamming intelligent recognition technology has increasingly become the key to electronic counter-counter measures. To achieve radar jamming open-set recognition with a small number of tagged samples, Generative adversarial network-based pseudolabeled convolutional neural network (GAN-PL-CNN) model is proposed in this paper. Firstly, radar jamming is modelled, and the radar jamming datasets with known and unknown types are established after time-frequency transformation and image processing. Then, the adaptive pseudolabeling method is used to achieve semi-supervised recognition of radar jamming by combining the learning capability of convolutional neural networks and the characteristics of pseudolabeling to increase labeled samples intelligently. Finally, generative adversarial networks are introduced to enhance the recognition ability of the model and the discrimination ability of the unknown type through the game between the generator and the discriminator in the iterative process. Experimental results show that the GAN-PL-CNN model achieves 89.7%-95.1% for known types and 30.7%-86.8% for unknown types at 3%-6% labeling rate. As the number of labeled samples increases, the recognition effect becomes better and has a greater ability to exclude unknown type. © 2021 IEEE.

关键词： convolutional neural network open-set recognition pseudo-label Radar active jamming

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Research on Location Algorithm Based on SAR and Infrared Fusion 1

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Yang, Yanjiao Qu, Guangzhou Li, Bo Ma, Meng Liu, ShuaiShuai Chi, QingXi Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Coopration Laboratory Beijing China

ISBN: (数字)9789811694929

ISBN: (纸本)9789811694912

Aiming at the problem of sins cumulative error correction, a dual-mode navigation algorithm based on infrared and SAR information fusion is studied. Radar system can provide SAR image and range information, and can measure height under certain conditions, but it cannot work in front. Infrared system can provide infrared image and optical axis direction of infrared lens. Based on the satellite positioning algorithm, this paper studies a fusion algorithm of radar SAR image and infrared. The image matching and measurement results are used to navigate to correct the accumulated errors of sins. simulation results show the effectiveness of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radar imaging

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A LSTM-based method for simulation execution validity evaluation 33

A LSTM-based method for simulation execution validity evalua...

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33rd European Modeling and simulation Symposium, EMSS 2021

作者： Wang, YuLun Li, Wei Li, Jing Control and Simulation Center Harbin Institute of Technology Harbin150080 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

This paper improves the concept of simulation execution validity and a methodology to assess the complex simulation system. As the complexity of the simulation system gradually rises, the magnitude of the simulation data obtained continues to increase, which makes it extremely difficult for experts to provide expert knowledge to evaluate the execution validity of the simulation system. More precisely, the essence of execution validity is to dig out the hidden relationships in the simulation time series data and complete the classification task whether it is valid. Considering that machine learning can better complete the two tasks of mining data features and classification, this paper adopts long short-term memory, a neural network used to process time series data, to evaluate the execution validity. Finally, an experiment is conducted on a simulation system, and the results show that the evaluation method based on LSTM can accurately evaluate the validity of the simulation system, and can greatly improve the efficiency of evaluation. © 2021 The Authors.

关键词： Long short-term memory

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Adversarial Example Attack and Defence of Object Recognition: A Survey

Adversarial Example Attack and Defence of Object Recognition...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Tan, Wei Zhao, Jiajia Liang, Xinkai Lu, Hanchen Song, Baogang Guan, Hao Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Mechano-Electronic Engineering Institute Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Deep learning-based intelligent object recognition algorithm has been widely applied in object detection, auto driving, ect. However, deep neural network is very vulnerable because of its high-dimensional linearization. The object recognition results can be easily mis-leaded through a tiny disturbance in the original image. This kind of sample that adds tiny disturbance to the original image is called adversarial example. The adversarial example attack is a factor that must be considered in the design of robust object recognition algorithm, that is, the need for adversarial defence. An in-depth understanding of adversarial attack can help the model defend successfully. In this survey, an introduction of adversarial attack and defense aiming to object recognition algorithm at the present stage is summarized, and the challenges to be faced and the development trend in the future are pointed out. © 2022 IEEE.

关键词： Object recognition

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Design of Reinforcement Learning Environment for Multiple UAV Area Coverage Search

Design of Reinforcement Learning Environment for Multiple UA...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Sun, Yinjiang Zhang, Rui Liu, Xiaoheng Liang, Wenbao Xu, Cheng Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811694912

Unmanned aerial vehicle (UAV) plays an increasingly important role both in civil and military fields. As a typical mission, area coverage search is a primary measure for reconnaissance and surveillance of unknown areas. Due to the limited information about the mission area, UAV needs adaptiveness to the dynamic changes of the area and makes decisions based on local observation. It puts higher demands on the response speed of UAV, and traditional planning methods are tough to be directly applied. The problem becomes more noticeable for multiple UAV because of the heterogeneous function and large scale, and new solutions are urgently required. In this paper, the feasibility of reinforcement learning (RL) for performing area coverage search is explored, and an environment is designed for RL training and evaluation. simulation results show that multiple UAV can cooperate and perform the mission with pretty high coverage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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complex-Valued Signal Capsule Network for Automatic Modulation Classification

Complex-Valued Signal Capsule Network for Automatic Modulati...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Yang, Shuyuan Feng, Zhixi Wang, Nengguo Bu, Yunong Hu, Hao Luoyang471003 China Xidian University Shannxi China Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811694912

Most current deep learning (DL) based AMC methods often require a large number of labeled samples to drive itself optimization study, so as to achieve a more superior performance. However, for many AMC tasks with a small amount of data, the AMC method based on deep learning still has many challenges, which makes the deep neural network model trained on a small dataset lack effective migration effect, leading to the failure to achieve better performance effect in the testing process. In this paper, we propose a novel AMC method based on complex-valued Signal Capsule Network (CSCN). Compared with other DL based methods, the proposed CSCN can achieve a better performance under the condition of small dataset and low SNR on the well-known deepsig dataset RML2016.10a and RML2016.10b. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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