Study on underwater navigation system using discrete hartley transform unscented Kalman filter for attitude estimation

2014 ◽  
Author(s):  
Haoqian Huang ◽  
Xiyuan Chen
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Discrete Hartley Transform ◽  
Hartley Transform ◽  
Underwater Navigation

Improved Unscented Kalman Filter Based on Decimation in Frequency Domain Fast Fourier Transform for Attitude Estimation Applied to Underwater Gliders

2014 ◽  
Vol 556-562 ◽  
pp. 4372-4375 ◽  
Author(s):  
Hao Qian Huang ◽  
Xi Yuan Chen ◽  
Hu Liu ◽  
Yuan Xu
Keyword(s):  
Fourier Transform ◽  
Kalman Filter ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Navigation System ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Underwater Gliders ◽  
Underwater Navigation ◽  
Better Than

In order to estimate the attitude fast and accurately for the underwater glider using the lower cost and lower power underwater navigation system, this paper designs a new underwater navigation system which is made up of the inertial sensors aided the magnetometer and proposes an improved unscented Kalman filter based on decimation in frequency domain fast Fourier transform (UKF-DF). UKF-DF makes better use of the estimate advantage of UKF in the nonlinear system, and in this basis DIF-FFT is integrated into UKF to increase the speed of calculation. Therefore, the attitude of a glider can be estimated fast and accurately. The real vehicle experiment is done to assess the performance of the proposed UKF-DF algorithm, the experimental results show that the attitude convergence of UKF-DF is better than EKF (extended Kalman filter) and the attitude estimated by UKF-DF is more precise than EKF.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

scholarly journals Attitude estimation of a motorcycle via Unscented Kalman Filter

2010 ◽  
Vol 43 (18) ◽  
pp. 511-516 ◽  
Author(s):  
Stefano Corbetta ◽  
Ivo Boniolo ◽  
Sergio M. Savaresi
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Attitude Estimation

scholarly journals A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5459 ◽  
Author(s):  
Xuliang Lu ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Haifeng Yan ◽  
Lei Si ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Hydraulic Support ◽  
Sensing System ◽  
Rock Structure ◽  
Gradient Descent Algorithm ◽  
Yaw Angle

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

scholarly journals Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2372 ◽  
Author(s):  
Antônio C. B. Chiella ◽  
Bruno O. S. Teixeira ◽  
Guilherme A. S. Pereira
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Ground Truth ◽  
Adaptive Strategy ◽  
Attitude Estimation ◽  
Covariance Matching ◽  
Adaptive Unscented Kalman Filter ◽  
Unit Quaternions ◽  
Abrupt Changes ◽  
Robust Adaptive

This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

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