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SINS/GNSS integrated navigation system based on maximum versoria filter

Chinese Journal of Aeronautics 2022年第8期35卷 168-178页

作者： Chunhui ZHAO Zhenyu YANG Xiaoran CHENG Jinwen HU Xiaolei Hou School of Automation Northwestern Polytechnical UniversityXi’an 710000China

In the missile-borne Strapdown Inertial Navigation System/Global Navigation Satellite System(SINS/GNSS)integrated navigation system,due to the factors such as the high dynamics,the signal blocking by obstacles,the signal intefereces,etc.,there always exist pulse interferences or measurement information interruptions in the satellite receiver,which make nonstationary measurement *** traditional Kalman Filter(KF)can tackle the state estimation problem under gaussian white noise,but its performance will be significantly reduced under nongaussian *** order to deal with the non-gaussian conditions in the actual missile-borne SINS/GNSS integrated navigation systems,a Maximum Versoria Criterion Extended Kalman Filter(MVC-EKF)algorithm is proposed based on the MVC and the idea of M-estimation,which assigns a smaller weight to the anomalous measurements so as to suppress the influence of anomalous measurements on the state estimation while maintaining a relatively low calculation ***,the integrated navigation simulation experiments prove the effectiveness and robustness of the proposed algorithm.

关键词： Extended Kalman filter gaussian kernel function Maximum versoria criterion Missile-borne SINS/GNSS integrated systems

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Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving

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Green Energy and Intelligent Transportation 2023年第2期2卷 40-55页

作者： Fang Liu Feng Xue Wanru Wang Weixing Su Yang Liu Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tiangong UniversityTianjin 300387China BBT-E-6 Complete Vehicle BMW Brilliance Automotive Ltd.Shenyang Liaoning 110098China

To meet the needs of the human-machine co-driving decision problem in the intelligent assisted driving system for real-time comprehensive driving ability evaluation of drivers,this paper proposes a real-time comprehensive driving ability evaluation method that integrates driving skill,driving state,and driving ***,by analyzing the driving experiment data obtained based on the intelligent driving simulation platform(the experiment can effectively distinguish the driver's driving skills and avoid the interference of driving style),the feature values that significantly represent driving skills and driving state are selected,and the time correlation between driving state and driving skills is pointed ***,the concept of relativity in comprehensive driving ability evaluation is further *** this concept,the natural driving trajectory dataset-HighD is used to establish the distribution map of feature values of the human driver group as the evaluation benchmark to realize the relative evaluation of driving skill and driving ***,HighD is used to establish a distribution map of human driver style feature values as an evaluation benchmark to achieve relative driving style ***,a comprehensive driving ability evaluation model with a“punishment”and“affirmation”mechanism is *** experimental comparative analysis shows that the evaluation algorithm proposed in this paper can take into account the driver's driving skill,driving state,and driving style in the real-time comprehensive driving ability evaluation,and draw differential evaluation conclusions based on the“punishment”and“affirmation”mechanism model to achieve a comprehensive and objective evaluation of the driver's driving *** can meet the needs of human-machine shared driving decisions for driver's driving ability evaluation.

关键词： Driving ability evaluation Driving style Unsupervised decision tree gaussian kernel function Human-machine shared control

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Research on Spectral Clustering Algorithms Based on Building Different Affinity Matrix

Research on Spectral Clustering Algorithms Based on Building...

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第25届中国控制与决策会议

作者： Xu Degang Zhao Panlei Gui Weihua Yang Chunhua Xie Yongfang Central South University College of Information Science and Engineering

ISBN: (纸本)9781467355339

As one of the most popular researches in the field of machine learning, spectral clustering algorithms have made great process in many different applications such as image processing. However, there are still some unsolved problems about spectral clustering algorithms, which should be immediately dealt with .These problems include how to build the affinity matrix, and how to deal with the eigenvectors. This paper mainly focuses on building the affinity matrix, which is the most important problem of spectral clustering algorithms. We propose four different methods to build the affinity matrix including the gaussian kernel function, the Minkowski function, the nearest-correlation function and the local scale function. Then, we develop four new algorithms to contrast the clustering results. Finally, we find that building appropriate local scale function is the most available method to formulate the affinity matrix for spectral clustering algorithm.

关键词： Spectral clustering gaussian kernel function Minkowski function Nearest-correlation function Local scale function.

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Joint Channel Coding based on LDPC Codes with gaussian kernel Reflection and CS Redundancy

Joint Channel Coding based on LDPC Codes with Gaussian Kerne...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Fei Zhong The College of Electronic Science and Engineering Jilin University Changchun Jilin P.R.China Shuxu Guo(Corresponding author) The College of Electronic Science and Engineering Jilin University Changchun Jilin P.R.China

This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC（Low-Density Parity-Check） *** information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC *** adopt gaussian kernel function of image segmentation as a *** results indicate,compared with LDPC algorithm,the algorithm presented in this paper can obviously make BER（bit error ratio） lower and improve system decoding performance,and different variance of gaussian kernel function can obtain different results.

关键词： compressed sensing(CS) joint channel coding low-density parity-check(LDPC) codes gaussian kernel function joint decoding

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