Article
Engineering, Aerospace
Matthew W. Givens, Jay W. McMahon
Summary: This work develops a novel sequential information filter formulation for computationally efficient visual-inertial odometry and mapping. By carefully constructing the square-root information matrix, the mean and covariance can be easily and accurately recovered throughout operation. Results show that this filter does not require explicit marginalization of past landmark states to maintain constant-time complexity.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2023)
Article
Automation & Control Systems
Ashvini Kulkarni, P. Augusta Sophy Beulet
Summary: GPS navigation is widely used for its advancements in positioning, location, and estimation. This article presents a state estimation algorithm using Kalman filter for indoor and outdoor activity, and compares different sensor fusion methods for accelerometer, gyroscope, and GPS data. The research demonstrates the robust performance of Kalman filter algorithms in diverse environments, and the potential for intelligent development of sensor-based navigation and monitoring.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Ronaldo Francisco Ribeiro Pereira, Felipe Proenca de Albuquerque, Luisa Helena Bartocci Liboni, Eduardo Coelho Marques Costa, Mauricio Carvalho de Oliveira
Summary: In this article, a nonlinear approach using the Kronecker product and the extended Kalman filter is proposed for the estimation of electrical parameters of transmission lines. The results show that this approach provides reliable and accurate estimation compared to other methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Cheng Pan, Jingxiang Gao, Zengke Li, Nijia Qian, Fangchao Li
Summary: A multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed to address the issue of inaccurate system models or statistical characteristics of noise affecting state estimation accuracy. The algorithm models measurement noise covariance matrix using inverse Wishart distribution and constructs scalar fading factor with remodified measurement noise covariance matrix and innovation covariance matrix estimated by exponential weighting method. Multiple fading factors are calculated to correct predicted error covariance matrix, leading to improved tracking ability compared to existing filters in target tracking simulations.
Article
Chemistry, Analytical
Yuxiang Pu, Xiaolong Li, Yunqing Liu, Yanbo Wang, Suhang Wu, Tianshuai Qu, Jingyi Xi
Summary: An improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper to solve the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems. The algorithm first reconstructs the observations based on the weighted positioning results and then utilizes the statistical properties to identify the NLOS observations. The ISTCKF is then constructed to mitigate the main positioning error and obtain accurate positioning result in the UWB system. Experimental results show that the ISTCKF algorithm significantly reduces the positioning error compared with other algorithms.
Article
Engineering, Electrical & Electronic
Chenglin Zhang, Wentao Shi, Zijun Gong, Qunfei Zhang, Cheng Li
Summary: This article investigates the target tracking problem using time delay and Doppler shift measurements obtained by bistatic sonar. The motions of the target, transmitter and receiver result in model bias if neglected, especially when the motion speeds are significant relative to underwater sound speed. To address this challenge, a motion-compensated extended Kalman filter (MC-EKF) is developed, which incorporates the motion effect into the measurement model by transforming the unknown real locations to expressions with respect to the target state. Theoretical analysis and simulations show that the proposed MC-EKF algorithm outperforms the motion-neglected EKF (MN-EKF) and reaches the posterior Cramer-Rao lower bound (PCRLB).
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Aerospace
Maryam Kiani, Reza Ahmadvand
Summary: This article introduces a new filter algorithm called STIF for estimation of nonlinear systems. The algorithm improves estimation accuracy and robustness through statistical linearization and adaptive fading factor. Simulation experiments demonstrate that STIF outperforms other algorithms in terms of convergence rate and estimation accuracy.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Energy & Fuels
Paul Takyi-Aninakwa, Shunli Wang, Hongying Zhang, Emmanuel Appiah, Etse Dablu Bobobee, Carlos Fernandez
Summary: This paper proposes a strong tracking adaptive fading-extended Kalman filter method based on the second-order resistor-capacitor equivalent circuit model for accurate estimation of the state of charge of lithium-ion batteries. Experimental results show that the proposed method outperforms the traditional extended Kalman filter in terms of accuracy and robustness under different working conditions and ambient temperatures.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Transportation Science & Technology
Mohammadreza Saeedmanesh, Anastasios Kouvelas, Nikolas Geroliminis
Summary: This study focuses on the traffic state estimation issue in urban networks modeled with MFD dynamics, using an estimation engine based on EKF theory to address real-time estimation challenges with limited data. The accuracy of the estimation is tested through micro-simulation, showing the methodology's versatility across different applications.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Information Systems
Jung Min Pak
Summary: SEKFB is a new filtering algorithm that addresses the issue of uncertain process-noise covariance in indoor localization using constant-velocity motion models. It runs multiple EKFs in parallel with different covariance hypotheses, selecting the most probable output using Mahalanobis distance evaluation, and has been shown through simulations to provide accurate and reliable localization without the need for careful selection of process-noise covariance.
Article
Engineering, Electrical & Electronic
Jingyi Wang, Yousef Alipouri, Biao Huang
Summary: In this article, the dual neural extended Kalman filter (DNEKF) is proposed to address model inaccuracies and noise assumption violations in multirate sensor fusion using two neural networks. The method improves process state and output predictions through simultaneous state and parameter estimations, benefiting from frequent but less accurate measurements and infrequent but more accurate measurements for neural network training. The effectiveness of the proposed method is demonstrated through numerical examples and an industrial application in compensating for inadequate process knowledge to enhance multirate sensor fusion performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Automation & Control Systems
Haining Ma, Zhengliang Lu, Xiang Zhang, Wenhe Liao
Summary: This study addresses a strongly nonlinear problem in CubeSat attitude estimation system caused by status mutation, proposing a multiple fading second-order central difference Kalman filter (MFSCDKF) to improve tracking performance and estimation accuracy simultaneously. Simulation results based on real telemetry data confirm the effectiveness of the proposed MFSCDKF for CubeSat attitude estimation in the presence of status mutation.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2021)
Article
Chemistry, Multidisciplinary
Yalun Luo, Zhaoming Li, Yurong Liao, Haining Wang, Shuyan Ni
Summary: This paper proposes a strong tracking cubature Kalman filter adaptive interactive multi-model algorithm for the tracking of hypersonic targets. By introducing fading factors and singular value decomposition, the algorithm improves tracking accuracy and convergence speed.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Tao Zhao, Wei Tong, Yao Mao
Summary: This article presents a hybrid nonsingleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high-precision photoelectric tracking system (PTS) to improve state estimation performance. The proposed H-NFSTKF is composed of a strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a nonsingleton FLS (NFLS). Different nonsingleton firing strength approaches are discussed and compared. The superiority of the proposed H-NFSTKF is verified through comparative simulation analyses and experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Shuanghu Qiao, Yunsheng Fan, Guofeng Wang, Dongdong Mu, Zhiping He
Summary: This paper proposes a simplified strong tracking square-root modified sliding window variational adaptive Kalman filter to address the performance deterioration of the Kalman filter in the presence of slowly time-varying and unknown measurement and process noise covariances. The proposed algorithm includes a modified sliding window variational adaptive Kalman filtering that corrects and smooths the previous states based on the latter states, reducing backward iterations and improving filtering accuracy and computational efficiency. Multiple fading factors are constructed to correct the one-step predicted error covariance matrix. The simulation results demonstrate that the proposed algorithm outperforms existing filters in tracking capacity and filtering accuracy of the one-step predicted error covariance matrix.
