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An Algorithm of INS Error Correction Base on the Intermittent Label Measurement and the Analysis of Its Influencing Factors

An Algorithm of INS Error Correction Base on the Intermitten...

引用

International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Peng, Siting Qian, Zhao Ma, Yichao Song, Zhichao Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811581540

Integrated navigation technology is widely used in aviation, aerospace, sailing and other ground-based navigation equipment, the inertial navigation system (INS) is the most common tool. In practice, the key to navigation is precise position, and the position is cooperated with the image matching. According to the label results after image matching, the position of sensor can be calculated. However, the label results cannot be get in every period, what’ more, he output results of the inertial navigation system (INS) is with big error, thus the article studied an algorithm of INS error correction based on the intermittent label measurement, in which the cubature kalman filter is used. After that, it also studied the influencing factor of the labels, such as the label error by match, the count of label, the area of label, thus the INS positioning error can be reduced in line with the engineering guarantee. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Error correction

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Remote Sensing Image Object Detection Algorithm Based on Receptive Field Enhancement

Remote Sensing Image Object Detection Algorithm Based on Rec...

引用

International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： MingJun, Liu Yun, Zhang Rui, Zhang Cheng, Xu Beijing Electro-Mechanical Engineering Institute Beijing10074 China Science and Technology on Complex System Control and Intelligent Agent Corporation Laboratory Beijing10074 China

ISBN: (纸本)9789811694912

Small object detection algorithm is always a difficult research topic in remote sensing images. A remote sensing image object detection algorithm which is based on receptive field enhancement is proposed in this paper. Firstly, the backbone network is the SSD net. In order to form the feature map pyramid, the receptive field enhanced convolution module is added in feature extraction. Then, the feature maps of the low-level and the high-level are spliced, so he output feature map retains the rich context information. Because it contains the information in the low-level feature map and the high-level feature map, which are location information and semantic information, respectively. Finally, based on the idea of dilated convolution, the receptive field enhancement module is designed to make the receptive field of the feature map larger. And the feature information is filtered to reduce the aliasing effect when the feature map is spliced. After the experimental result on remote sensing image dataset NWPU vhr-10, the average detection accuracy of the algorithm is 90%, which improves the object detection accuracy, especially the detection accuracy of the small objects. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A Novel Attitude controller for Hypersonic Aircraft Based on FR-PI2

A Novel Attitude Controller for Hypersonic Aircraft Based on...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Fan, Jiajun Li, Tianyi Guo, Xian Hao, Mingrui Sun, Mingwei College of Artificial Intelligence Nankai University Tianjin300350 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Bejing100074 China

ISBN: (纸本)9789811581540

Due to the uncertainties and various noises of the Hypersonic Aircraft(HA), traditional method of designing attitude controllers based on accurate models faces immense challenges. To solve those proposed urgent problems effectively, in this paper, a innovative data-driven RL algorithm called FR-PI2 is proposed for the task of Hypersonic Aircraft attitude control, wherein several key techniques are introduced. Firstly, Hypersonic Aircraft attitude control is transformed into a stochastic optimal control problem, and the numerical solutions of the proposed stochastic optimal control problem are obtained with a path-integral-based method, called Policy Improvement with Path Integrals(PI2). Secondly, a model-free policy parameterization method with PID controller is integrated into the PI2 algorithm to ensure a low dependency of the Hypersonic Aircraft system model. Thirdly, a brand-new sample filtration method is proposed to guarantee rapid convergence, and an online optimization technology called rolling optimization is applied to guarantee optimality and real-time performance. Two series of experiments are carried out and analyzed to demonstrate the outstanding performance of the FR-PI2 algorithm. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Design and simulation of a two-stage linear inverted pendulum control system based on fuzzy LQR control

Design and simulation of a two-stage linear inverted pendulu...

引用

International Symposium on Autonomous systems (ISAS)

作者： Siting Peng Jun Ren Lei Li Yichao Ma Zhichao Song Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory The Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

This paper introduces in detail the origin of the development of automatic control methods, applications and current research hotspots. It also introduces some control methods commonly used in industrial and agricultural production, and expounds the current application hotspots and new advanced control methods that are developing rapidly. The background, current situation and classification of the inverted pendulum platform are introduced in detail. In the first chapter, the LQR method in modern control theory is introduced in detail. The influence of parameters is analyzed through simulink space modeling and simulation control experiment. At the same time, the real-time experiment is also done in combination with the hardware platform. In the second chapter, the fuzzy control theory in intelligent control is introduced in detail. By combining fuzzy control with LQR method, suitable LQR parameters are calculated by pole assignment. After proper adjustment, reasonable fuzzy rules are adopted and adaptive fuzzy control principle is applied to adjust the control situation in real time. Using simulink space modeling, a good simulation control effect is obtained. Finally, the summary is made.

关键词： Fuzzy control Analytical models Adaptation models Quantization (signal) Production Aerospace electronics Real-time systems

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Radar Target Selection Strategy Based on Improved Subjective Bayesian Reasoning

Radar Target Selection Strategy Based on Improved Subjective...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Li, Wen Pei, Hucheng Qu, Guangzhou Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9789811694912

In order to deal with the influence of dilution passive decoy jamming on target selection of UAV radar, and to enable UAV radar have more independent decision-making ability to acquire the naval ship correctly in an ocean battlefield scene containing single real ship and multiple passive decoys, a method which established the mathematical simulation model of jamming scene, and used the model as the fitness function to calculate the adequacy measurement parameters of subjective Bayesian reasoning through genetic optimization algorithm is proposed. This method is applied to UAV radar target selection, compared with traditional target selection methods using single target feature, this method can improve the accuracy of ship acquisition by comprehensive using multi-dimensional target features. Moreover, this method can further improve the joint acquisition accuracy by setting multiple adequacy measurement parameters for different radars, which prove that the method can achieve the purpose of improving the success rate of UAV radar against dilution passive decoy jamming. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radar

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UAV Typical Index Evaluation Method Based on Improving Evidence Theory

UAV Typical Index Evaluation Method Based on Improving Evide...

引用

International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Geng, Huapin Suo, Bin Tong, Jiahui Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9789811694912

It is an effective way to get more accurate results of UAV performance evaluation by fusing the data of infield test and outfield test. The common Bayes multi-source data fusion method can make comprehensive use of multiple information types while ensuring the decision risk as small as possible, but there are some risks, such as insufficient data utilization and small sample data being "submerged" by large sample data in the fusion. Due to the different scale and credibility of multi-source data, a non-uniform distribution probability sampling method is proposed based on evidence theory for the same sample space to avoid the risk of small sample data being "drowned" by large sample data;meanwhile, for the situation that the fusion weight mainly depends on the evaluation of experts is not objective enough, a comprehensive weight confirmation method of multi-source test data is proposed based on credibility measurement so as to realize the comprehensive evaluation of performance indicators based on multi-source data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Reinforcement learning-based three-dimensional cooperative guidance law

Reinforcement learning-based three-dimensional cooperative g...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Zhen Gu Zeqi Zhang Honglong Kang Jianfeng Li Keyuan Yue Shenmin Song Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

An innovative three-dimensional cooperative guidance law for striking stationary target, based on proximal policy optimization (PPO), is introduced. This novel guidance law directly correlates engagement state information with the navigation ratio of proportional navigation guidance (PNG). Initially, the cooperative homing guidance problem is transformed into a Markov decision process, and the reward function incorporates considerations for the zero-effort-miss (ZEM) and the consensus error of time-to-go. Subsequently, the cooperative homing guidance problem is transposed into the framework of reinforcement learning (RL). Ultimately, the effectiveness of the cooperative homing guidance solution is validated through numerical simulations, encompassing agent model training and Monte Carlo cases.

关键词： Training Monte Carlo methods Navigation Markov decision processes Reinforcement learning Numerical simulation Robustness

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Research on Depth Estimation Method of Single Aerial Image 6

Research on Depth Estimation Method of Single Aerial Image

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6th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Gao, Wang Pu, Lin Li, Lei Deng, Fei Bie, Lingzhi Beijing Institute of Mechanical and Electrical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Wuhan University School of Geodesy and Geomatics Wuhan China Wuhan Tianjihang Information Technology Co. Ltd Wuhan China

ISBN: (纸本)9781665460033

Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial image data sets to study the depth estimation. We used FCRN and LapDRN to extract the depth estimation results of visible aerial image and infrared aerial image under global and single object respectively, and compared the results extracted from the two types of networks by qualitative and quantitative methods. The results show that the depth estimation accuracy of visible aerial image is higher than that of infrared image, but the increase rate of infrared image accuracy index is higher than the increase rate of image depth range. The image depth estimation results extracted by LapDRN method are superior to FCRN in accuracy, structure consistency and edge clarity, which indicates that LapDRN is more effective in the estimation of aerial image depth information. © 2023 IEEE.

关键词： Antennas

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An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision

An Adversarial Defense Algorithm Based on Triplet Network an...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Zheng, Haonan Zhao, Jiajia Tan, Wei School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In the field of artificial intelligence, neural network is one of the key technologies used for image classification and recognition. However, recent work has demonstrated that deep neural networks are easily attacked by adversarial examples to make misjudgments. Adversarial examples are almost indistin-guishable from normal examples and yet cannot be classified correctly by neural networks. The existence of adversarial examples is a major obstacle to the practical application and deployment of neural networks, so the research on adversarial defense algorithms is an important topic in the field of AI security. This paper proposes an adversarial example defense algorithm based on a triplet network and voting decision mechanism. Firstly, two neural networks with different structures are trained based on normal dataset. Secondly, the first network is fine-tuned using the adversarial examples generated by these two networks, resulting in a third neural network. Then, these three neural networks are used as sub-networks in parallel to construct a triplet network. Through adversarial training and differences in structures, the transferability of adversarial examples among the three sub-networks is weakened. Finally, the final classification result is obtained by majority voting, based on the parallel output results of the three sub-networks. Through the complementarity between these three sub-networks, the defense against adversarial examples is realized. The experimental results demonstrate the effectiveness of this algorithm. © 2022 IEEE.

关键词： Deep neural networks

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A Comparative Study on Adversarial Attacks for Visible and Infrared Images

A Comparative Study on Adversarial Attacks for Visible and I...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Guo, Haiwang Tan, Wei Zhao, Jiajia School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing100074 China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In recent years, the booming development of artificial intelligence has driven the development and progress of society. With its excellent performance, deep learning has been widely used in many fields, including some security-sensitive fields such as unmanned systems, auto-driving and so on. Research has found that deep neural networks are vulnerable to adversarial samples, which can lead to misclassification of neural networks and can cause serious safety incidents. Hence, the study of adversarial samples has received increasing attention, and researchers have conducted studies from different perspectives, and relevant research results have emerged. However, most of these studies have focused on visible images, and few have studied infrared images, which play an important role in military and other important fields. In this paper, a comparative study on adversarial attacks for visible and infrared images is presented. A number of adversarial attack experiments are conducted on ResNet18 and VGG16 models for visible and infrared images. By comparing the experimental results, the characteristics of infrared adversarial samples are derived relative to those with visible adversarial samples. Then, a gradient attack method from a frequency domain perspective is proposed. The adversarial perturbation noise is added to the low frequency component of images. It is verified experimentally that the proposed method improves the transferability of infrared adversarial samples compared to some baseline methods. © 2022 IEEE.

关键词： Deep neural networks

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