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Adaptive nonlinear Kalman filters based on credibility theory with noise correlation

Chinese Journal of Aeronautics 2024年第6期37卷 232-243页

作者： quanbo ge Zihao SONG Bingtao ZHU Bingjun ZHANG School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing 210044China School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang 330013China School of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China School of Electronic and Information Engineering Tongji UniversityShanghai 200092China

To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Filter(KF)theory considering noise *** method first extends credibility to the Unscented Kalman Filter(UKF)and Extended Kalman Filter(EKF)based on the credibility ***,an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,further expanding its application scenarios,and the derivation process of the formula theory is ***,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental *** simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.

关键词： Kalman filter Extended Kalman Filter(EKF) Unscented Kalman Filter(UKF) Credibility Noise correlation

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A Novel Adaptive Kalman Filter Based on Credibility Measure

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 103-120页

作者： quanbo ge Xiaoming Hu Yunyu Li Hongli He Zihao Song IEEE the School of Automation Nanjing University of Information Science and TechnologyNanjing 210044 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing 210044China KTH Royal Institute of Technology Stockholm 10044Sweden the School of Automation Hangzhou Dianzi UniversityHangzhou 310000China the Testing Institute Chinese Flight Test EstablishmentXi’an 710000China the School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang 330000China

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly *** previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate *** can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman ***,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate *** at this,the concept of credibility is adopted to discuss the inconsistency problem in this *** order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the ***,the trust factor can not be directly computed since the TMSE cannot be found for practical *** on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the *** importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high ***,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are *** analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise ***,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

关键词： Credibility expectation maximization-particle swarm optimization method(EM-PSO) filter calculated mean square errors(MSE) inaccurate models Kalman filter Sage-Husa true MSE(TMSE)

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Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2284-2297页

作者： Zhongyuan Zhao Zhiqiang Yang Luyao Jiang Ju Yang quanbo ge the School of Automation Nanjing University of Information Science and Technologyand also with Jiangsu Key Laboratory of Big Data Analysis Technology the School of Mechanical Engineering Shanghai Jiao Tong University IEEE

This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback(OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.

关键词： Differential privacy distributed online optimization(DOO) federated learning one-point residual feedback(OPRF) time-varying unbalanced graphs

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Information Fusion Algorithm Based on Adaptive Cubature Strong Tracking Filter 36

Information Fusion Algorithm Based on Adaptive Cubature Stro...

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第36届中国控制会议

作者： Rui Wang quanbo ge Jianwen Meng Logistics Engineering College Shanghai Maritime University The Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi University

ISBN: (纸本)9781538629185

The adaptive cubature strong tracking information filter(ACSTIF),which combines the strong tracking filter with the variational Bayesian method,has a good performance when the sudden change of state and the unknown variance of measurement noise ***,it also remains two ***,the iteration of estimating the unknown variance of measurement noise is not very ***,it is designed for single sensor system so it’s measure range and the accuracy is not very *** at these two problems,this paper proposed a sequential fusion algorithm based on improved adaptive cubature strong tracking information *** optimize the algorithm by modifying the process of estimating the unknown variance of measurement noise and combining the improved ACSTIF algorithm with sequential filtering *** analysis shows that the improved ACSTIF has a better performance than the original ***,a four dimensional simulation example is given to verify the validity and accuracy of the analysis results obtained in this paper.

关键词： Kalman filter adaptive filter strong tracking filter sequential filtering fusion

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Cramer-Rao lower bound-based observable degree analysis

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Science China(Information Sciences) 2019年第5期62卷 29-31页

作者： quanbo ge Tianxiang CHEN Hongli HE Zhentao HU School of Automation Hangzhou Dianzi University Chinese Flight Test Establishment College of Computer and Information Engineering Henan University

Dear editor,Controllability and observability are basic concepts in modern control theory that are based on Kalman filtering (KF)[1, 2]. In particular, observability is closely related to state estimation ability [2–4]. The indicator called observable degree(OD) has been used to quantitatively measure the degree of observability [5–7]. However, limited by

关键词： ODA Cramer-Rao lower bound-based observable degree analysis

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Multi-layer collaborative optimization fusion for semi-supervised learning

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Chinese Journal of Aeronautics 2023年第11期36卷 342-353页

作者： quanbo ge Muhua LIU Jianchao ZHANG Jianqiang SONG Junlong ZHU Mingchuan ZHANG School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China Qiandao Lake Institute of Science Hangzhou 311799China

Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image ***,traditional CTA approaches face challenges such as high computational complexity and low classification *** overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification ***,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training ***,we conduct various experiments to verify the performance of the proposed *** results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.

关键词： Collaborative training Fusion Image classification K-means algorithm Semi-supervised learning

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OPTIMAL DISTRIBUTED FUSION ALGORITHM WITH ONE-STEP OUT-OF-SEQUENCE ESTIMATES

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Journal of Electronics(China) 2008年第4期25卷 529-538页

作者： ge quanbo Wen Chenglin College of Automation Hangzhou Dianzi University Hangzhou 310018 China

The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of *** existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so *** order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first *** from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and ***,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this *** a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction ***,the smooth result of theOOSE problem also has good fusion *** analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.

关键词： Sensor networks Distributed fusion One-step delay Kalman filtering Out-Of-Sequence Measurements (OOSM) Out-Of-Sequence Estimates (OOSE)

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Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

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Chinese Journal of Aeronautics 2022年第5期35卷 40-52页

作者： quanbo ge Zhongcheng MA Jinglan LI Qinmin YANG Zhenyu LU Hong LI School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi UniversityHangzhou 310018China State Key Laboratory of Industrial Control Technology and the College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and TechnologyNanjing 210044China Chinese Flight Test Establishment Xi’an 710089China

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement *** this paper,first,a correlation multiplicative measurement noise model is *** is able to more accurately represent the measurement error caused by the distance sensor dependence ***,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is *** improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative *** practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even *** address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is ***,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

关键词： Adaptive CKF Correlated noise Estimation accuracy Multiplicative noise Performance analysis Target tracking

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A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： quanbo ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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Nonlinear Fusion Using Quantized Measurements and Cubature Particle Filter

Nonlinear Fusion Using Quantized Measurements and Cubature P...

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第25届中国控制与决策会议

作者： Xianfeng Tang Binlei Guan quanbo ge Xiaoliang Xu Modem Educational Technology Center Zhejiang University School of Electron and Information Engineering Ningbo University of Technology Institute of System Science and Control Engineering Hangzhou Dianzi University School of Computer Science and Technology Hangzhou Dianzi University

ISBN: (纸本)9781467355322

Consider the nonlinear estimation fusion problem for dynamic stochastic process in sensor networks. Due to bandwidth or energy constraints, only quantized messages of the original information from local sensor are available. For a class of vector state-vector observation model, a quantized cubature particle filter （CPF） method is presented in this paper. Firstly, each sensor quantizes each component of the measurement verbatim and sends to fusion center （FC）. Subsequently, FC compresses the quantized messages from local sensors in best linear unbiased estimation （BLUE） fusion rule. Finally, CPF is used to obtain a state estimation. Computer simulations show effectiveness of the developed method.

关键词： Nonlinear fusion Wireless sensor network Quantization Cubature particle filter

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