Article
Engineering, Electrical & Electronic
Hang Geng, Zidong Wang, Alireza Mousavi, Fuad E. Alsaadi, Yuhua Cheng
Summary: This paper investigates a novel outlier-resistant filtering problem for networked systems with dead-zone-like censoring using the weighted try-once-discard protocol (WTODP). The Tobit model is employed to describe the censoring phenomenon, and the WTODP is used to decide the transmission sequence of sensors to avoid collisions. A saturation function is applied in the Tobit Kalman filter structure to handle measurement outliers, and an upper bound on the filtering error covariance is obtained using matrix inequality approach. The effectiveness of the proposed algorithm is verified through practical examples.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Statistics & Probability
Kostas Loumponias, George Tsaklidis
Summary: This paper discusses the use of Kalman filtering in the presence of censored process measurements, utilizing a Tobit Type I model to handle the censored data. Bayesian estimates for multidimensional state vectors are provided through a recursive Kalman filtering algorithm. Experimental results demonstrate that the proposed method effectively reduces computational costs and improves overall accuracy when compared to other filtering methodologies for synthetic and real data sets.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Automation & Control Systems
Baoan Li, Yonghong Tan, Lei Zhou, Ruili Dong
Summary: A robust-nonsmooth Kalman filtering approach is proposed for stochastic sandwich systems with dead-zones, which guarantees an upper bound on the variance of filtering error. The approach describes the system using a stochastic nonsmooth state-space function and utilizes a linearization approach based on nonsmooth optimization to approximate the system within a bounded region around the equilibrium point. The method also handles model uncertainty and performs state estimation for the system through robust-nonsmooth Kalman filtering.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Hang Geng, Zidong Wang, Fuad E. Alsaadi, Khalid H. Alharbi, Yuhua Cheng
Summary: This paper investigates the multi-sensor filtering fusion problem under stochastic uncertainties with the use of the Round-Robin protocol (RRP). Various sources of uncertainties, such as censored observations, dynamical biases, and additive white noises, are considered and modeled in the study. The fusion process involves a two-stage approach where sensor observations are first used to generate local estimates before being fused at a central fusion center.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2021)
Article
Optics
Wei Wang, Naibao He, Keming Yao, Jinwu Tong
Summary: In this study, the robustness and accuracy improvement of the Kalman filter algorithm are examined. By analyzing the impact of feedback coefficients on initial alignment, an improved algorithm is proposed and verified through simulation and experiments.
Article
Engineering, Electrical & Electronic
Jiahao Zhang, Su Zhao
Summary: This article proposes a distributed adaptive Tobit Kalman filter (DATKF) for discrete time networked systems with multiple sensors, sensor delays, and censored measurements. By using an adaptive probability selection strategy and weighted average consensus (WAC), fused state estimates are obtained, and the precision of information fusion is enhanced through a weighted rule.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2022)
Article
Psychology, Multidisciplinary
Jan R. Magnus, Anatoly A. Peresetsky
Summary: This passage provides an explanation of the Dunning-Kruger effect as a statistical artifact, without the need for any psychological explanation. By specifying a simple statistical model that accounts for the (random) boundary constraints, the model fits the data almost perfectly.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Automation & Control Systems
Hang Geng, Zidong Wang, Xiaojian Yi, Fuad E. Alsaadi, Yuhua Cheng
Summary: In this article, the Tobit Kalman filtering problem for a class of discrete time-varying fractional-order systems with measurement censoring and stochastic nonlinearities under the Round-Robin protocol (RRP) is investigated. A protocol-based fractional Tobit Kalman filter is designed, with statistical means used to characterize the stochastic nonlinearities. The paper addresses the issues related to RRP, fractional dynamics, and stochastic nonlinearities in the filter design process.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Chemistry, Analytical
Jijun Geng, Linyuan Xia, Jingchao Xia, Qianxia Li, Hongyu Zhu, Yuezhen Cai
Summary: This paper proposes a 3D indoor positioning method based on smartphone MEMS sensors, utilizing a robust adaptive cubature Kalman filter algorithm to estimate pedestrian heading and distinguishing pedestrian behavior patterns based on step frequency information. This method achieves accurate three-dimensional positioning coordinates of indoor pedestrians and meets the demand for location services on personal intelligent user terminals.
Article
Chemistry, Analytical
Asma Asif, Sithamparanathan Kandeepan, Robin J. Evans
Summary: Passive bistatic radar research is crucial for accurate 3D target tracking, especially when facing missing or low-quality bearing information. To address the bias introduced by traditional extended Kalman filter (EKF) methods, we propose the use of unscented Kalman filter (UKF) to handle nonlinearities in 3D tracking with range and range-rate measurements. Moreover, we incorporate the probabilistic data association (PDA) algorithm with UKF to deal with cluttered environments. Through extensive simulations, we demonstrate the successful implementation of the UKF-PDA framework, which effectively reduces bias and significantly enhances tracking capabilities in passive bistatic radars.
Article
Engineering, Multidisciplinary
HuiXia Li, Hang Guo, Yuhui Qi, Linkun Deng, Min Yu
Summary: The proposed method utilizes multi-sensor fusion, correction with UKF, and calibration methods such as ZUPT and ZARU to effectively reduce position deviation and improve positioning accuracy of PDR.
Article
Computer Science, Information Systems
Ioannis Krikidis, Constantinos Psomas
Summary: This letter investigates a receiver architecture that performs energy harvesting and estimation of a Gauss-Markov linear process simultaneously using the received signal. We analyze three communication scenarios: static channel, Rayleigh block-fading channel, and high power amplifier (HPA) nonlinearities at the transmitter side. Theoretical results for minimum mean square error and average harvested energy are provided, and the tradeoff between estimation quality and harvested energy is characterized. It is shown that channel fading improves estimation performance, while HPA requires an extended Kalman filter and significantly affects both estimation and harvesting efficiency.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Automation & Control Systems
Haifen Li, Zupeng Zhou, Xuebin Li, Xiaoyu Zhang
Summary: A new nonsmooth Kalman filtering method is proposed for noise suppression of compound sandwich systems with backlash and dead zone. The filter can automatically switch among different operating zones and shows better convergence and accuracy in state estimation compared to conventional methods.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Xuxiang Fan, Gang Wang, Jiachen Han, Yinghui Wang
Summary: In this article, a new Kalman filter algorithm called background-impulse KF (BIKF) is proposed. The measurement noise is divided into background noise and impulse noise, and the parameters are dynamically calculated using the expectation-maximization algorithm to effectively handle the impact of impulse noise.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Public, Environmental & Occupational Health
Yu-Han Chiu, Lan Wen, Sean McGrath, Roger Logan, Issa J. Dahabreh, Miguel A. Hernan
Summary: This article introduces two approaches for evaluating model specification when using the noniterative conditional expectation (NICE) parametric g-formula in the presence of censoring, and describes how to correctly compute natural-course estimates of time-varying covariate means.
AMERICAN JOURNAL OF EPIDEMIOLOGY
(2023)
Article
Chemistry, Organic
Xinglin Ye, Jian Huang, Zhihong Deng, Yiyuan Peng
Summary: An efficient and green methodology was developed for the synthesis of a series of 2-(hetero)aryl-4-phosphorylated quinazolines via a palladium-catalyzed C-O/P-H cross-coupling reaction, providing an alternative protocol for introducing phosphorus groups to quinazoline compounds at the C4 position through C-O activation.
SYNTHESIS-STUTTGART
(2022)
Article
Automation & Control Systems
Bo Wang, Jingyuan Jia, Zhihong Deng, Mengyin Fu
Summary: This article proposes an improved method of evidence theory for the state monitoring of a gas regulator station. By using a back-propagation neural network to judge evidence conflicts, introducing a relative conflict factor to modify evidence, and calculating an adaptive time attenuation factor to reduce accumulated error, the dynamic fusion of time-domain information is achieved. The feasibility and effectiveness of the method are verified through experiments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Jinwen Wang, Zhihong Deng, Zhidong Meng, Kai Shen
Summary: This article proposes a magnetoresistive sensor error compensation method using geometry-constraint contour scaling, which improves compensation accuracy by calculating the shortest distance and scaling the contour.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Jinwen Wang, Zhihong Deng, Kai Shen, Mengyin Fu
Summary: This paper proposes a statistical confidence domain data driven based fast in-flight alignment method to improve the alignment accuracy and alignment time by using K matrix to construct a filter model and defining noise evaluation indexes.
Article
Automation & Control Systems
Xiaoxue Feng, Shuhui Li, Yue Wen, Feng Pan
Summary: Considering the heavy-tailed non-Gaussian property of the Student T distribution, this paper models the heavy-tailed non-Gaussian noises induced by strong maneuvering targets as the Student T distribution. A cost function based on the Student T distribution is designed, and the Student T-based Maximum Correntropy Unscented Kalman filter (TMCUKF) is proposed using this cost function and the maximum correntropy criterion. The algorithm shows strong suppression ability to heavy-tailed non-Gaussian noise and improves tracking accuracy.
Article
Computer Science, Artificial Intelligence
Haoqing Wang, Huiyu Mai, Yuhang Gong, Zhi-Hong Deng
Summary: Meta-learning aims to use previous task knowledge to facilitate learning novel tasks, and task augmentation can increase the diversity of training tasks to improve the generalization capability of meta-learning models.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Jinwen Wang, Xuan Xiao, Zhihong Deng, Mengyin Fu
Summary: Due to the complex and harsh launching and flying environment, gyros strapdown to projectile is prone to failure and cannot accurately sense projectile angular rate. This article establishes an online estimation model of projectile angular rate, which describes the mathematical relationship among the output information of magnetoresistive sensors, the angular rate of gyro output, and attitude matrix. In order to achieve real-time estimation of projectile angular rate, an improved sparrow search algorithm (ISSA) is proposed, which combines adaptive dynamic step strategy with dynamic compression search strategy, improving the traditional SSA. This method takes angular rate as the optimization object and uses the ISSA diagonal rate estimation model to solve and obtain high-precision estimation of angular rate. The simulation results verify the correctness and feasibility of the online estimation model of projectile angular rate using only magnetoresistive sensors, and the precision of angular rate estimation based on the ISSA is better than that of the traditional intelligent optimization algorithm.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Shengwu Zhao, Xuan Xiao, Yu Wang, Zhihong Deng
Summary: In this article, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed to address the issues caused by the initial position error of the inertial navigation system (INS), gravity measurement error, and gravity background map similarity in existing gravity matching algorithms. The IPFBGMF takes into account both the value and change characteristic of gravity measurements and proposes a novel position acquisition method based on the gravity measurement feature, which reduces the impact of the initial position error of INS. Additionally, a new concept of direction measurement using the heading angle of INS is introduced to optimize the weight of particles in the particle filter (PF), mitigating the influence of gravity measurement error and gravity background map similarity. Furthermore, the robustness of the improved PF with precise position is demonstrated. A navigation strategy is designed for the application of the proposed algorithms. Simulation results show that IPFBGMF achieves the highest positioning accuracy compared to traditional gravity matching algorithms.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Shengwu Zhao, Xuan Xiao, Zhihong Deng, Lei Shi
Summary: The article proposes a gravity-matching algorithm based on a correlation filter to reduce the impact of gravity measurement error and initial position error on matching accuracy. The trajectory shape of the inertial navigation system is used as a constraint to improve matching accuracy. Experimental results demonstrate the effectiveness of the method in improving matching accuracy under initial position error and gravity measurement error.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Bo Wang, Tianjiao Li, Zhihong Deng, Mengyin Fu
Summary: Selection of gravity gradient matching area is crucial for underwater gravity gradient-aided navigation. Existing methods overlook the high-resolution characteristics of the gravity gradient, leading to inaccurate selection. Thus, a frequency domain matching area selection method based on the high-resolution characteristics of the gravity gradient is proposed. This method extracts the high-frequency information of the gravity gradient reference map using wavelet transform and establishes a gravity gradient wavelet transform model. The proposed method utilizes morphological image texture segmentation to extract densely textured areas as the matching areas. Simulation results demonstrate that this method achieves a matching rate higher than 90% by obtaining texture density, amplitude, and direction in the matching area. Compared to existing methods, the proposed method improves the accuracy of the matching areas and reduces the computational burden to less than 10% of the existing algorithm. Furthermore, the matching rate increases when the trajectory is more perpendicular to the texture inside the matching area.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shaohua Wu, Zhihong Deng, Aimin Li, Jian Jiao, Ning Zhang, Qinyu Zhang
Summary: In this paper, the timeliness performance of a downlink wireless communication system with non-orthogonal multiple access (NOMA) is investigated. An adaptive transmission policy is proposed to achieve a tradeoff between timeliness and reliability in NOMA systems. The system adapts power allocation and determines whether to transmit old or new packets based on the Age of Information (AoI) status and ACK/NACK feedback signal. An optimal policy is obtained to minimize the average AoI by formulating the problem as a Markov Decision Process (MDP). Additionally, a near-optimal policy based on Lyapunov Drift function and a greedy policy to minimize the maximal expected AoI are proposed.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Zixuan Ma, Bo Wang, Liu Huang, Fang Cui, Zhihong Deng, Mengyin Fu
Summary: This study proposes a neural network-based matching method to improve the accuracy of navigation and positioning by extracting multidimensional gravity features. The method expands the sequence of gravity anomaly values into a two-dimensional feature map containing time-series features using the Gramian angular fields method and performs matching using an affine transformation and a Siamese convolutional neural network model. Simulation results and practical tests demonstrate that the proposed method achieves more precise location results compared to traditional matching algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Bo Wang, Tianjiao Li, Zhihong Deng, Mengyin Fu
Summary: A gravity gradient comprehensive image matching algorithm based on local gray-level co-occurrence matrix (GLCM) is proposed, which can make better use of multicomponent gravity gradient reference map information and improve matching accuracy. The algorithm has high accuracy and better robustness, and does not have strict requirements on the change of gravity gradient characteristics in the navigation area.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Aerospace
Shuhui LI, Zhihong Deng, Xiaoxue Feng, Ruxuan He, Feng Pan
Summary: This paper addresses the state estimation problem for systems with heavy-tailed and skew non-Gaussian noise by proposing a robust Bayesian filter and smoother based on a hierarchical Gaussian state space model. The proposed algorithms approximate the system state and unknown noise parameters. The probabilistic graphical form of the multivariate skew t distribution is utilized to transform the estimation problem.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Proceedings Paper
Computer Science, Software Engineering
Taotao Li, Mingsheng Wang, Zhihong Deng, Dongdong Liu
Summary: This paper presents SEPoW, a secure and efficient sidechains construction for proof of work (PoW) sidechain systems. SEPoW addresses the challenges of centralization, inefficiency and insecurity faced by sidechains, and achieves desirable security properties. Comparative evaluation with other state-of-the-art PoW sidechains protocols demonstrates that SEPoW significantly reduces proof size.
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT III
(2022)
