Article
Engineering, Electrical & Electronic
Jiangbo Song, Wanqing Li, Xiangwei Zhu, Zhiqiang Dai, Chengxin Ran
Summary: The application of SINS/LBL system is effective in solving the accumulated position error of AUVs. However, height positioning in this system is generally unstable. To address this issue, an adaptive method based on an improved Sage-Husa adaptive filter is proposed, which improves the precision and stability of vertical positioning in the SLT system.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Songlai Han, Chaoliang Ni, Jiazhen Lu
Summary: A new method is proposed to improve the performance of MEMS inertial navigation systems (INS) by using a fiber optic gyro (FOG) to monitor the triad of MEMS gyros and compensate for their errors in real time with a well-designed Kalman filter. A state-space model of a single-FOG monitored MEMS INS is developed, and the observability of the model is analyzed for the Kalman filter. Simulated and experimental tests are conducted to evaluate the effectiveness of this approach. The results show that it can effectively reduce the errors of MEMS gyros and greatly improve the initial alignment and navigation performances of MEMS INS.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Qinke Qi, Youming Li, Qiang Guo
Summary: This article proposes a robust source localization method using time delay and Doppler shift measurements, taking into account sensor location errors and motion effects. By using convex relaxation and bias reduction, the proposed method achieves accurate localization with reduced bias.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Marine
Rongyan Zhou, Jianfeng Chen, Weijie Tan, Chang Cai
Summary: This paper investigates a sensor selection scheme for optimal target localization in Underwater Wireless Sensor Networks (UWSN) using three-dimensional Angle of Arrival (AOA) estimation. The study presents a new 3-D AOA-based localization measurement model considering correlated noises and Gaussian priors. The performance of the proposed method is evaluated through simulations, where it outperforms reference methods and approaches the exhaustive search method.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Haoqian Huang, Jiacheng Tang, Cong Liu, Bo Zhang, Bing Wang
Summary: A novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information of autonomous underwater vehicle (AUV) under the complex marine condition. The algorithm assumes velocities follow Gaussian distribution and utilizes augmentation method for simplification, resulting in better estimation accuracy and robustness compared to other algorithms according to experiment results.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Aerospace
Ankit Goel, Dennis S. Bernstein
Summary: In applications of state estimation, it is often necessary to restrict the state correction to a specific subspace corresponding to the measurement location. This paper presents the injection-constrained unscented Kalman filter (IC-UKF) and the injection-constrained retrospective cost filter (IC-RCF) to address this problem. The performance of these filters is evaluated numerically, and their accuracy and suboptimality relative to full-state output-error injection are compared.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2022)
Article
Engineering, Electrical & Electronic
Wence Shi, Jiangning Xu, Ding Li, Hongyang He
Summary: This paper proposes an improved Kalman filter (VBRAKF) that effectively solves the attitude estimation problem in a complex underwater environment. The filter achieves robustness by suppressing outliers based on Mahalanobis distance and adaptivity by estimating the uncertain measurement noise covariance using variational Bayesian approximation. Experimental results demonstrate the superiority of the proposed method over traditional methods.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Hee-Seung Kim, Lingyi Zhang, Adam Bienkowski, Krishna R. Pattipati
Summary: This paper introduces stochastic gradient descent algorithms for noise covariance estimation in adaptive Kalman filters, which are significantly faster than batch methods with similar or better root mean square error. These algorithms are also applicable to non-stationary systems.
Article
Computer Science, Information Systems
Andrea Petroni, Gaetano Scarano, Roberto Cusani, Mauro Biagi
Summary: Underwater acoustic communications face challenges such as time variability, narrow bandwidth, multipath, frequency selective fading, and the Doppler effect. Orthogonal Frequency Division Multiplexing (OFDM) is an effective solution to these impairments, but requires accurate channel knowledge. This work proposes an adaptive OFDM scheme that predicts channel state information using a Kalman-like filter to optimize communication parameters. Extensive simulations using real channels and interference show the effectiveness of this scheme in terms of rate and reliability, although it increases complexity compared to conventional mechanisms.
Article
Automation & Control Systems
Dipankar Maity, David Hartman, John S. Baras
Summary: This paper considers the classical sensor scheduling problem for linear systems, where only one sensor is activated at a time. It is shown that the sensor scheduling problem is closely related to the sensor design problem, and the solution to the sensor scheduling problem can be extracted from an equivalent sensor design problem. A convex relaxation method is proposed for the sensor design problem, and a reference covariance trajectory is obtained as a result. A covariance tracking algorithm is then designed to obtain an approximate solution to the sensor scheduling problem using the reference covariance trajectory. By decomposing the problem into a convex sensor design problem and a covariance tracking problem, the proposed framework circumvents the computational complexity of the sensor scheduling problem. Theoretical justification and a sub-optimality bound for the proposed method are provided using dynamic programming. The effectiveness of the proposed method is validated through several experiments.
Article
Engineering, Electrical & Electronic
Haoqian Huang, Shuang Zhang, Di Wang, Keck-Voon Ling, Fan Liu, Xiufeng He
Summary: This paper proposes a Bayesian-based adaptive Kalman filter with Gaussian-inverse-Wishart mixture (BAKF-GIWM) distribution to address the issue of inaccurate position estimation in complex underwater environment when sensor measurement information is lost. By detecting measurement loss and using the variational Bayesian method to determine the system state vector and statistical information of system noise, the proposed algorithm improves accuracy and efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Chemistry, Multidisciplinary
Xiang Song, Haoqian Huang, Chunxiao Ren
Summary: In various applications of AUVs, acquiring real-time location and speed information is necessary but challenging due to the complicated marine environment. To address this issue, a switched variational estimation filtering (SVEF) algorithm, combining the advantages of Gaussian filtering and variational estimation, is proposed.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Lanhua Hou, Xiaosu Xu, Yiqing Yao, Di Wang
Summary: This paper presents an M-estimation-based Improved Interacting Multiple Model (IIMM) algorithm for inertial navigation system (INS)/Doppler velocity logs (DVL) integrated method to directly apply in complex underwater environment without empirical parameters. The proposed algorithm adaptively handles the time-varying measurement noise covariance and generates an adaptive state transition probability matrix based on Bayesian estimation theory. The M-estimation-based filter is employed as a primary model to improve the robustness of the algorithm.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Xiang Yu, Hong-De Qin, Zhong-Ben Zhu
Summary: This paper proposes a linear time-varying single beacon navigation model with an unknown ESV, which can achieve global convergence under the condition of system observability. By adopting a Kalman filter to estimate the model state, it addresses the navigation performance issue caused by ESV setting error.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Mathematics, Applied
Stefan Sremac, Hugo J. Woerdeman, Henry Wolkowicz
Summary: In semidefinite programming, the discrepancy between forward error and backward error can lead to a poor optimal solution despite small residual in optimality conditions. Sturm provided an upper bound on forward error in terms of backward error and singularity degree, while this work presents a method to bound the maximum rank of all optimal solutions. The relationship between singularity degree and slow convergence is explored, with large singularity degree being a sufficient condition for slow convergence in a certain type of paths.
SIAM JOURNAL ON OPTIMIZATION
(2021)