Article
Engineering, Electrical & Electronic
Gongxu Liu, Baoguo Yu, Lingfeng Shi, Ruicai Jia
Summary: This article proposes a virtual-measurement-combined extended Kalman filter (VMC-EKF) method that combines the carrier's motion state for attitude estimation, eliminating the requirement for uniform observability. Through a series of experiments, it is shown that the method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with state-of-the-art methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Yiming Liu, Binjie Qin, Rong Li, Xintong Li, Anqi Huang, Haifeng Liu, Yisong Lv, Min Liu
Summary: This article introduces a novel multimodal quasi-contactless HR sensor that combines face and head motion disturbances and fuses rPPG and BCG signals for accurate heart rate estimation. Experimental comparisons demonstrate that the proposed sensor is more robust and accurate than the state-of-the-art single-modal sensors for heart rate estimation.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Biochemical Research Methods
Amir H. Abolmasoumi, Mohammad Mohammadian, Lamine Mili
Summary: This paper proposes a revised version of the GM-UKF for state estimation in GRNs with different deviations from assumptions. The GM-UKF outperforms other methods for all outlier types, while the H-8-UKF is appropriate for changes in noise powers.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Yuming Chen, Wei Li, Yuqiao Wang
Summary: A novel fast indirect in-motion coarse alignment method is proposed, utilizing the output of GPS and SINS to construct the measurement model and using the rotation vector as the attitude parameterization. An adaptive Student's t-based Kalman filter is introduced to handle challenges of measurement noise distribution deviation and inaccurate noise covariance matrix.
Article
Physics, Multidisciplinary
Batubayaer Ou-Yun, Zhao Yue-Jin, Kong Ling-Qin, Dong Li-Quan, Liu Ming, Hui Mei
Summary: This paper proposes a non-contact heart rate detection method based on head movement information. The method effectively suppresses the noise caused by head rigid rotation and improves the accuracy of heart rate detection.
ACTA PHYSICA SINICA
(2022)
Article
Engineering, Biomedical
Juan Cheng, Bicheng Yue, Rencheng Song, Yu Liu, Chang Li, Xun Chen
Summary: Imaging ballistocardiography (iBCG) is a novel technique that utilizes video-based technology to measure heart rate (HR) without direct physical contact with the body. This paper proposes a novel method for iBCG motion artifact removal (MAR) by reconstructing raw iBCG signals from facial regions of interest (ROIs), removing rigid motion artifacts using adaptive filtering, compressing the signals via principal component analysis (PCA), and removing residual non-rigid motion artifacts through canonical correlation analysis (CCA). The proposed method achieves superior performance compared to existing iBCG methods, especially in the presence of motion artifacts.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
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
Rajamani Doraiswami, Lahouari Cheded
Summary: The study introduces a novel multiple Kalman filtre-based scheme for fault diagnosis and tolerance in non-linear systems, using piecewise linear parameter-varying Box-Jenkins dynamic models and emulators to improve accuracy and robustness.
IET CONTROL THEORY AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Chao Zhao, Wenru Zeng, Dandan Hu, Hong Liu
Summary: The study demonstrated a wrist-worn heart rate monitor using a single accelerometer, with signal decomposition algorithms and Kalman smoothing to remove motion artifacts and noise, providing more accurate heart rate estimation within +/- 8.86 bpm, longer battery life and better wearing comfort compared to PPG.
IEEE SENSORS JOURNAL
(2021)
Article
Instruments & Instrumentation
Hiroki Yoshikawa, Masayuki Hayashi, Akira Uchiyama, Teruo Higashino
Summary: This paper proposes a method for estimating the reliability of heart rate measurement by establishing a correlation model between heart rate changes and body movement, using an improved Kalman filter for estimation and compensation. The method successfully removes low-reliability measurements and improves the accuracy of heart rate measurement.
SENSORS AND MATERIALS
(2022)
Article
Multidisciplinary Sciences
Kevin Course, Prasanth B. Nair
Summary: This study presents a state estimation technique based on approximate Bayesian approach, which learns the missing terms and state estimation in the mathematical model. It enables state estimation for physical systems with partially or completely unknown dynamical equations.
Article
Automation & Control Systems
Chen Liu, Gang Wang, Xin Guan, Chutong Huang
Summary: In this paper, a framework that combines M-estimation and information-theoretic learning (ITL)-based Kalman filter under impulsive noises is proposed. The proposed framework inhibits the divergence trend of ITL-based Kalman filters at low kernel bandwidth and improves the performance at large kernel bandwidth. Monte Carlo simulations demonstrate the robustness and effectiveness of the proposed algorithm.
Article
Engineering, Electrical & Electronic
Batu Candan, Halil Ersin Soken
Summary: This article introduces two novel covariance-tuning methods for a robust Kalman filter algorithm to solve attitude estimation problem using only IMU measurements. The proposed methods can adapt to external accelerations to enhance robustness, and adjust the measurement noise covariance adaptively to improve attitude estimation accuracy.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Marine
Joel Reis, Pedro Batista, Paulo Oliveira, Carlos Silvestre
Summary: This paper addresses the problem of estimating the heave motion of a platform using biased measurements of an accelerometer. A general framework is developed to exploit properties of trigonometric functions for designing a linear system to represent wave amplitudes, phase shifts, and sensor bias implicitly. The observability of the system is analyzed and a discrete-time linear time-varying Kalman filter is implemented with global asymptotic stability guarantees. Realistic numerical examples validate the proposed methodology, including the accurate representation of a continuous wave spectrum within an ocean context.
Article
Geochemistry & Geophysics
Xuanyu Qu, Xiaoli Ding, You-Lin Xu, Wenkun Yu
Summary: Structural health monitoring (SHM) is crucial for the safety of large civil engineering structures, and GNSS-based technology is commonly used to obtain real-time 3D displacement information. A new robust multi-rate Kalman filter-based approach is proposed to integrate GNSS and other sensor observations more reliably, mitigating the influence of outliers. Experimental results have shown significant improvement in accuracy and vibration frequency capture compared to conventional methods.
JOURNAL OF GEODESY
(2023)
Article
Engineering, Mechanical
Sakthi Kumar Arul Prakash, Tobias Mahan, Glen Williams, Christopher McComb, Jessica Menold, Conrad S. Tucker
JOURNAL OF MECHANICAL DESIGN
(2020)
