Article
Computer Science, Information Systems
Rui Sun, Yeying Dai, Qi Cheng
Summary: In order to address the vulnerability issues of the traditional EKF-based GNSS/IMU/Vision fusion scheme to NLOS and multipath contaminated GNSS, as well as low-quality vision measurement, we propose an adaptive weighting strategy. By adjusting the weights of vision and GNSS measurements adaptively based on the chi-square test statistic, the proposed algorithm achieves improved accuracy compared to traditional EKF-based GNSS/IMU fusion and compared EKF-based GNSS/IMU/Vision fusion.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Andrea Tuveri, Fernando Perez-Garcia, Pedro A. Lira-Parada, Lars Imsland, Nadav Bar
Summary: In this study, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to estimate key process variables in a fed-batch bacterial cultivation process. The results demonstrate precise estimation of biomass and substrate consumption, particularly when adapting the process covariance matrix to account for model inaccuracies during the feeding phase.
JOURNAL OF PROCESS CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Yangtianze Tao, Stephen Shing-Toung Yau
Summary: In this paper, we propose a novel outlier-robust iterative extended Kalman filtering (OR-IEKF) framework based on nonlinear regression formulation of update step. The OR-IEKF framework introduces a new Kalman-type update step with reweighted prediction covariance and reweighted observation noise covariance, which can eliminate large outliers caused by unknown outlier noises. By employing robust cost functions, three algorithms are derived to solve the special nonlinear regression problems. The performances of these new filters are evaluated in a simulation study of a nonlinear system.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Xiaoxiong Liu, Yinglong Wang, Nan Yang, Yue Yang
Summary: Long-term convergent and accurate state estimation is crucial for unmanned aerial vehicles. However, low-cost sensors often have poorer measurement accuracy and noise compared to high-precision sensors. To improve the state estimation accuracy and reliability of UAVs using low-cost sensors, a two-step multisensor fusion estimator called federated mixed sampling Kalman filter (FMSKF) is proposed. The proposed algorithm utilizes a multisensor integrated navigation model array and a mixed sampling Kalman filter to optimize the state estimation and achieve desirable navigation performance.
IEEE SENSORS JOURNAL
(2023)
Article
Chemistry, Analytical
Ning Liu, Wenhao Qi, Zhong Su, Qunzhuo Feng, Chaojie Yuan
Summary: This paper presents an extended Kalman filter for a low-cost MARG sensor system. By combining a two-stage gradient descent algorithm and the extended Kalman filter, the filter's performance is improved, showing better anti-interference performance, dynamic performance, and measurement accuracy.
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
Telecommunications
Inam Ullah, Siyu Qian, Zhixiang Deng, Jong-Hyouk Lee
Summary: This paper introduces an EKF-based localization algorithm using edge computing to achieve higher accuracy and wider coverage. Simulation results show that the proposed algorithm is more accurate compared with current state-of-the-art localization algorithms.
DIGITAL COMMUNICATIONS AND NETWORKS
(2021)
Article
Plant Sciences
Meibo Lv, Hairui Wei, Xinyu Fu, Wuwei Wang, Daming Zhou
Summary: With the aging society and modern agriculture development, the use of agricultural robots for large-scale agricultural production will be a major trend in the future. To solve the problem of navigation system failure caused by external noise and other factors, a multi-sensor fusion method based on agricultural scenes is proposed, utilizing a loosely coupled extended Kalman filter algorithm to reduce interference from the external environment. Experimental results demonstrate the high accuracy and robustness of the proposed algorithm in case of sensor failures, showing better accuracy and robustness on agricultural datasets compared to other algorithms.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Acoustics
Erlend Magnus Viggen, Havard Kjellmo Arnestad
Summary: This article investigates an enhanced model for sound radiation near and below coincidence. Unlike the classical model, this model fully respects conservation of energy by balancing the radiated power with power lost from the guided wave underlying the vibration. The model successfully validates against exact solutions for leaky A0 Lamb waves around coincidence.
JOURNAL OF SOUND AND VIBRATION
(2023)
Article
Instruments & Instrumentation
Jian Chen, Shaojing Song, Yang Gu, Shanxin Zhang
Summary: This paper aims to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm. The proposed algorithm effectively reduces fingerprint mismatching and improves positioning accuracy by adding a weighted factor, providing a feasible solution for indoor positioning.
Article
Robotics
Seyed Fakoorian, Angel Santamaria-Navarro, Brett T. Lopez, Dan Simon, Ali-akbar Agha-mohammadi
Summary: This work presents a resilient and adaptive state estimation framework, AMCCKF, for robots operating in perceptually-degraded environments, which is able to robustly handle corrupted measurements and adjust filter parameters online for improved performance. Two methods are developed, modifying noise models and kernel bandwidth based on measurement quality, with differences in computational complexity and overall performance. The framework is validated through real experiments on aerial and ground robots, forming part of the solution used in the DARPA Subterranean Challenge by the COSTAR team.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Dongjiao He, Wei Xu, Fu Zhang
Summary: This article proposes a generic method to formulate the iterated error-state extended Kalman filter (IESEKF) on manifolds, aiming to facilitate its deployment for on-manifold systems. The proposed framework has the advantages of an equivalent error-state system and decoupled manifold constraints, and is implemented as a toolkit in C++. Two filter-based lidar-inertial navigation systems are used to verify the effectiveness of the proposed framework.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Chemistry, Multidisciplinary
El Yamine Dris, Mourad Bentahar, Redouane Drai, Abderrahim El Mahi
Summary: This paper presents a real-time monitoring methodology to identify the location of acoustic emission sources generated by microcracks within an aluminum plate under tensile load. The approach combines time-frequency analysis with an extended Kalman filter to determine the spatial coordinates of the acoustic emission sources.
APPLIED SCIENCES-BASEL
(2022)
Article
Acoustics
Hao Chen, Huifang Chen, Ying Zhang, Wen Xu
Summary: A decentralized method for estimating the two-dimensional ocean current field using underwater acoustic sensor networks is proposed. It integrates state-of-the-art estimation techniques and employs a distributed estimator for improved efficiency and robustness. The method shows feasibility and robustness in Monte Carlo simulations, with potential for adaptation to fast-varying dynamics or reduced sensor measurement rates through increased communication rounds.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2021)
Article
Chemistry, Multidisciplinary
Ricardo Piza, Rafael Carbonell, Vicente Casanova, Angel Cuenca, Julian J. Salt Llobregat
Summary: This paper presents a sensor fusion approach based on extended Kalman filter for path-following control of a holonomic mobile robot with four mecanum wheels. The approach addresses the issues of nonuniform measurement rates and sensor failures, and improves control performance by modifying the Pure Pursuit path-tracking algorithm.
APPLIED SCIENCES-BASEL
(2022)
