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Grouping bi-chi-squared method for pulsar navigation experiment using observations of Rossi X-ray timing explorer

Chinese Journal of Aeronautics 2023年第1期36卷 386-395页

作者： Haifeng SUN Jianyu SU Zhongwen DENG Lirong SHEN Weimin BAO Xiaoping LI Liansheng LI Zhe SU Wencong WANG School of Aerospace Science and Technology Xidian UniversityXi’an 710126China Shaanxi Key Laboratory of Space Extreme Detection Xi’an 710126China Peng Cheng Laboratory Shenzhen 518000China Academy of Advanced Interdisciplinary Research Xidian UniversityXi’an 710126China Beijing Institute of Control Engineering Beijing 100190China Institute of Space Electronic Information Technology Xi’an 710100China Shandong Institute of Aerospace Electronic Technology Yantai 264032China

X-ray pulsar-based navigation is a revolutionary technology which is capable of providing the required navigation information in the solar system. Performing as an important pulsar-based navigation technique, the Significance Enhancement of Pulse-profile with Orbit-dynamics(SEPO)can estimate orbital elements by using the distortion of pulse profile. Based on the SEPO technique,we propose a grouping bi-chi-squared technique and adopt the observations of Rossi X-ray Timing Explorer(RXTE) to carry out experimental verification. The pulsar timing is refined to determine spin parameters of the Crab pulsar(PSR B0531+21) during the observation periods, and a short-term dynamic model for RXTE satellite is established by considering the effects of diverse *** results suggest that the position and velocity errors are 16.3 km(3σ) and 13.3 m/s(3σ) with two adjacent observations split by one day(exposure time of 1.5 ks), outperforming those of the POLAR experiment whose results are 19.2 km(3σ) and 432 m/s(3σ). The approach provided is particularly applicable to the estimation of orbital elements via using high-flux observations.

关键词： Model buildings Navigation analysis Navigation testing Pulsars RXTE observations

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A Semantic Interpretation Method for Deep Neural Networks Based on Knowledge Graphs

A Semantic Interpretation Method for Deep Neural Networks Ba...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jingjing Xu, Song Wang, Lina Beijing Aerospace Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (纸本)9781665465335

Despite the great success of deep neural networks in many fields, the lack of interpretability has severely limited their wide application in security-sensitive tasks. Although current interpretable methods of deep neural networks such as visualization, class activation mapping, and sensitivity analysis can help users intuitively understand the inner working mechanism of the neural networks to some extent, they are either too coarse in explanation or too complex in explanation form to read easily. In order to use semantic information which is more understandable and close to human thought to interpret the deep neural network and increase the readability of the interpretation,we propose a semantic interpretation method of deep neural network based on knowledge graph. The method takes the VGG16 network as an example, and by mining the key neuronsof the neural network, construct semantic dictionaries and knowledge maps of key neurons, and automatically generate human-understandable semantic explanatory statements based on the knowledge maps. The method provides a new idea to improve the transparency of the operation process of deep neural networks, and also provides a clearer reference basis for pruning and tuning of deep neural networks. © 2022 IEEE.

关键词： Deep neural networks

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A Low-noise Frequency-agile Ku-band Frequency Synthesizer for Wideband Frequency Hopping Communication System

A Low-noise Frequency-agile Ku-band Frequency Synthesizer fo...

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2023 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2023

作者： Luo, Ding Shao, Huaizong Zhu, Xueyong Yang, Yuanwang Information and Communication Engineering University of Electronic Science and Technology of China China Laboratory of Electromagnetic Space Cognition and Intelligent Control China

ISBN: (纸本)9798350308761

In this paper, a low-noise frequency-agile Ku-band frequency synthesizer for the wideband frequency hopping communication system is proposed. Three analog PLL (phase locked loop), two of which constitute an in-loop mixing architecture scheme, are used to provide the carrier frequency in wide low-noise frequency coverage. With out-of-loop and in-loop mixing architecture, the carrier frequency source implemented in the paper has superior performance on frequency jitter, which can be shown as the phase noise level less than -107.3dBc/Hz@1kHz when the carrier frequency is between 12 GHz to 15 GHz in 2 MHz steps. The spurious level is below -64 dBc, and the frequency hopping time is less than 40 us. © 2023 IEEE.

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Fixed-time position coordinated tracking control for spacecraft formation flying with collision avoidance

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Chinese Journal of Aeronautics 2021年第11期34卷 182-199页

作者： Minglei ZHUANG Liguo TAN Kehang LI Shenmin SONG Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China Research Center of Basic Space Science Harbin Institute of TechnologyHarbin 150001China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100190China

In this paper,the fixed-time stability of spacecraft formation reconfiguration(position tracking)is ***,a novel nonsingular terminal sliding mode surface is designed and based on which a fixed-time coordinated controller is designed to keep the closed-loop system states have a finite settling time bounded by some predefined ***,another nonsingular terminal sliding mode surface is designed by combining the artificial potential function and the aforementioned sliding surface,which meets the mutual distance constraint during transition process among spacecraft when it is *** another coordinated controller with fixed-time observer considering mutual distance constraint is presented,which guarantees the closed-loop system states stable also in bounded settling ***,simulation results are shown to validate the correctness of the proposed *** is worth mentioning that the control schemes also work even though there is a properly limit on the control input.

关键词： Collision avoidance Disturbance rejection Fixed-time stability Sliding mode control spacecraft formation flying

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Exploring Adversarial Robustness of Deep State space Models 38

Exploring Adversarial Robustness of Deep State Space Models

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qi, Biqing Luo, Yiang Gao, Junqi Li, Pengfei Tian, Kai Ma, Zhiyuan Zhou, Bowen Department of Electronic Engineering Tsinghua University China Shanghai Artificial Intelligence Laboratory China Department of Control Science and Engineering Harbin Institute of Technology China School of Mathematics Harbin Institute of Technology China

Deep State space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing Adversarial Robustness (AR) and has been validated on various traditional DNN architectures. However, its effectiveness in improving the AR of SSMs remains unclear. While many enhancements in SSM components, such as integrating Attention mechanisms and expanding to data-dependent SSM parameterizations, have brought significant gains in Standard Training (ST) settings, their potential benefits in AT remain unexplored. To investigate this, we evaluate existing structural variants of SSMs with AT to assess their AR performance. We observe that pure SSM structures struggle to benefit from AT, whereas incorporating Attention yields a markedly better trade-off between robustness and generalization for SSMs in AT compared to other components. Nonetheless, the integration of Attention also leads to Robust Overfitting (RO) issues. To understand these phenomena, we empirically and theoretically analyze the output error of SSMs under AP. We find that fixed-parameterized SSMs have output error bounds strictly related to their parameters, limiting their AT benefits, while input-dependent SSMs may face the problem of error explosion. Furthermore, we show that the Attention component effectively scales the output error of SSMs during training, enabling them to benefit more from AT, but at the cost of introducing RO due to its high model complexity. Inspired by this, we propose a simple and effective Adaptive Scaling (AdS) mechanism that brings AT performance close to Attention-integrated SSMs without introducing the issue of RO. Our code is available at Robustness-of-SSM. © 2024 Neural information processing systems foundation. All rights reserved.

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PIGWN: Physics-Informed Graph WaveNet for Airport Flight Traffic Flow Prediction 6

PIGWN: Physics-Informed Graph WaveNet for Airport Flight Tra...

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6th International Conference on Industrial Artificial intelligence, IAI 2024

作者： Yang, Zhichao Zhu, Yinghao Niu, Ziyue Huang, Yanru Pan, Chengwei Dong, Xiwang School of Artificial Intelligence Beihang University Beijing China China Academy of Launch Vehicle Technology Beijing China School of Artificial Intelligence Institute of Unmanned System School of Automation Science and Electrical Engineering Beijing China Beihang University Science and Technology on Aircraft Control Laboratory Beijing China

ISBN: (纸本)9798350356618

The continuous growth of global travel demand leads to the continuous rise of airport flight flow, leading a rising need to accurately predict the airport flight traffic flow. Current research predominantly employs Graph Convolutional Neural Networks (GCN) to model intra-airport traffic changes, but overlooks comprehensive modeling of air traffic routes and temporal dimensions. Furthermore, existing models rely on data-driven approaches lacking a deep understanding of traffic change mechanisms, resulting in poor performance in new scenarios. To solve these problems, we propose a deep learning network framework Physics-Informed Graph WaveNet (PIGWN) based on multi-scale feature data fusion and constructs two new airport domain data sets. We integrated a physics-based module prior to GraphWaveNet to learn and integrate multi-scale spatio-temporal information of airports, embedding physical formulas reflecting airport traffic changes. The physics learning module fuses multi-scale spatio-temporal feature data considering the time delay and traffic conservation relationship between different airports. The Graph WaveNet (GWN) structure learns and predicts multi-scale spatio-temporal feature data, and uses the time-space convolution block model to capture and model spatio-temporal features. PIGWN model has certain theoretical significance and practical application value in the field of airport flight flow prediction, which realizes the accurate prediction of airport flight flow and provides certain decision support and reference for air traffic management. We have made the code publicly available at https://***/yzc0912/PIGWN. © 2024 IEEE.

关键词： Data fusion

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CNS/INS Integrated Navigation Method Based on Improved Adaptive CKF Algorithm 49

CNS/INS Integrated Navigation Method Based on Improved Adapt...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wang, Dong Yang, Jing Xiong, Kai Li, Wenling School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350331820

Aiming at the problem that measurement model uncertainty restrains navigation accuracy in the celestial/ inertial (CNS/INS) integrated navigation system, a kind of CNS/INS integrated navigation method based on improved adaptive cubature Kalman filter (CKF) is proposed. In order to mitigate the influence of installation error and axis disturbance error of star camera, angular distance between starlight vector of stars and line of sight vector of space targets is employed to establish the measurement model;the measurement noise covariance matrix (MNCM) is estimated online to lower the model uncertainty;adaptive adjustment coefficient is constructed based on squared Mahalanobis distance (SMD) of measurement innovation to adjust forgetting factor online to further improve robustness and adaptability of the method. Simulation results demonstrate that the proposed method can achieve higher navigation accuracy compared to standard and classical method under measurement model mismatch. © 2023 IEEE.

关键词： Uncertainty analysis

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Approximately Invertible Neural Network for Learned Image Compression

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IEEE Transactions on Image Processing 2025年 34卷 3041-3055页

作者： Gao, Yanbo Li, Shuai Fu, Meng Lv, Chong Yang, Zhiyuan Cai, Xun Yuan, Hui Ye, Mao Shandong University School of Software Jinan250100 China Shandong University-WeiHai Research Institute of Industrial Technology Shandong University Weihai264209 China Shandong University School of Control Science and Engineering Jinan250100 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250100 China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Learned image compression has attracted considerable interests in recent years. An analysis transform and a synthesis transform, which can be regarded as coupled transforms, are used to encode an image to latent feature and decode the feature after quantization to reconstruct the image. Inspired by the success of invertible neural networks in generative modeling, invertible modules can be used to construct the coupled analysis and synthesis transforms. Considering the noise introduced in the feature quantization invalidates the invertible process, this paper proposes an Approximately Invertible Neural Network (A-INN) framework for learned image compression. It formulates the rate-distortion optimization in lossy image compression when using INN with quantization, which differentiates from using INN for generative modelling. Generally speaking, A-INN can be used as the theoretical foundation for any INN based lossy compression method. Based on this formulation, A-INN with a progressive denoising module (PDM) is developed to effectively reduce the quantization noise in the decoding. Moreover, a Cascaded Feature Recovery Module (CFRM) is designed to learn high-dimensional feature recovery from low-dimensional ones to further reduce the noise in feature channel compression. In addition, a Frequency-enhanced Decomposition and Synthesis Module (FDSM) is developed by explicitly enhancing the high-frequency components in an image to address the loss of high-frequency information inherent in neural network based image compression, thereby enhancing the reconstructed image quality. Extensive experiments demonstrate that the proposed A-INN framework achieves better or comparable compression efficiency than the conventional image compression approach and state-of-the-art learned image compression methods. © 1992-2012 IEEE.

关键词： Image coding

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Dependence of Plastic Stability on 3D Interface Layer in Nanolaminated Materials

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Acta Metallurgica Sinica(English Letters) 2022年第11期35卷 1759-1764页

作者： Shuimiao Jiang Lichen Bai Qi An Zhe Yan Weiming Li Kaisheng Ming Shijian Zheng Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology School of Materials Science and EngineeringHebei Universityof TechnologyTianjin 300130China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130China

The nanolaminated materials generally exhibit poor plasticity due to the fast onset of shear *** interface structure is an eff ective approach for enhancing plasticity via postponing or suppressing the shear ***,we introduce 4 nm thick CuNb 3D amorphous interface layers and Nb 3D crystalline interface layers in Cu nanolaminated materials,*** situ micro-pillar compression tests show that samples with crystalline interface layers exhibit shear instability,while the samples with amorphous interface layers display uniform *** the plastic deformation of the singlecrystal crystalline interface layer is anisotropic,except for well-aligned slip systems,dislocations on other slip systems have a poor ability to transmit the 3D crystalline interface layer,leading to localized dislocations pileups and shear *** contrast,the amorphous interface layer which is plastically isotropic accommodates dislocations from arbitrary slip systems of the matrix,which can alleviate the stress concentrations at the interface,and thus suppresses the shear instability.

关键词： Multilayers Interface Plastic deformation Dislocation Amorphous interface

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Attitude Feedforward Compensation control for Multistage Satellite with Scanning Mechanism

Attitude Feedforward Compensation Control for Multistage Sat...

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2022 Chinese Automation Congress, CAC 2022

作者： Fan, Mao Tang, Liang Guan, Xin Guo, Zixi Hao, Renjian Zhang, Kebei Beijing Institute of Control Engineering Key Laboratory of Science and Technology on Space Intelligent Control Beijing China

ISBN: (纸本)9781665465335

In this paper, a multi-step prediction feed-forward compensation attitude control method based on the conservation of angular momentum is designed for multistage satellite with a two-dimensional scanning mechanism. Firstly, the dynamic model of multistage satellite with a scanning mechanism is established by the Newton-Euler method. Secondly, a multi-step prediction feedforward compensation method based on the conservation of angular momentum is proposed for the discrete multistage control system to solve the control period mismatching and the measurement delay. Finally, the numerical simulation shows that the control method proposed in this paper improves the attitude stability and pointing accuracy of multistage satellite by 3-4 times. © 2022 IEEE.

关键词： Attitude control

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