A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays

Xu, Xiaolong; Tian, Xiaohui; Zhou, Lelai; Li, Yibin

doi:10.1016/j.measurement.2018.11.062

Cited by 29 publications

(17 citation statements)

References 26 publications

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“…Widely applied in civilian and military fields, attitude and heading reference system (AHRS) has been attracting significant attention [1][2][3]. AHRS aims mainly to track the attitude of an object based on the outputs of the Micro-Electronic-Mechanical-System (MEMS)-based Inertial Measurement Unit (IMU), which consists of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer [4,5].…”

Section: Introductionmentioning

confidence: 99%

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Tao

Song

Weng

2021

Mathematical Problems in Engineering

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In this paper, a robust heading determination method is proposed for low-cost attitude and heading reference system (AHRS) aided by the global positioning system (GPS). As compared with the traditional GPS/SINS-integrated navigation-based heading determination method, in the proposed method, the heading information obtained from the GPS velocity outputs is first incorporated into the observation vector, which constructs a novel completed GPS/SINS integration framework and greatly improves the observability of azimuthal misalignment in the Kalman filter. Moreover, a multivariate constrained total least square (MCTLS) method is proposed and integrated into the completed integration framework to deal with the measurement error in both input and output data of GPS velocity measurement model, which improves the accuracy of the observed heading information and yields a robust heading estimation at each time instant. Simulation and experiment results demonstrate that the proposed robust heading determination method can outperform the related state-of-the-art methods for the GPS-aided attitude and heading reference system.

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Section: Introductionmentioning

confidence: 99%

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Tao

Song

Weng

2021

Mathematical Problems in Engineering

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“…In these specific applications, the work of different attitude determination algorithms is classified according to the following categories. Among them, representative research studies include attitude determination utilizing the different MEMS sensors [11][12][13][14], different Kalman filter methods for attitude estimation [15,16], sensor fusion algorithms for attitude determination [17,18], and intelligent control method strategy combined with filters [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming that the object does not move or moves at a constant speed, the roll angle and pitch angle can be estimated by the strapdown triaxial of the accelerometer [14]. Magnetometers are widely used for estimating the yaw angle in AHRS [15] or estimating the ballistic roll in the projectile [16]. e attitude determination approaches utilizing different sensors separately are difficult to provide reliable estimation due to sensor errors and dynamic environmental variations.…”

Section: Introductionmentioning

confidence: 99%

“…e Extended Kalman Filter (EKF) [4] and Adaptive EKF (AEKF) [7,14] are developed for nonlinear system state estimation in navigation system. Unscented Kalman Filter (UKF) [5,9] and Adaptive UKF (AUKF) [15,16] are widely used for sensor fusion algorithms. e methods mentioned above can achieve accuracy and adaptability for attitude estimation.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment

Wang

Meng

Guan

2020

Mathematical Problems in Engineering

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Focusing on the issue of attitude tracking for low-cost and small-size Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) in high dynamic environment, an Adaptive Unscented Kalman Filter (AUKF) method combining sensor fusion methodology with Artificial Neural Network (ANN) is proposed. The different control strategies are adopted by fusing multi-MEMS inertial sensors under various dynamic situations. The AUKF attitude determination approach utilizing the MEMS sensor and Global Positioning System (GPS) can provide reliable estimation in these situations. In particular, the adaptive scale factor is used to adaptively weaken or enhance the effects on new measurement data according to the predicted residual vector in the estimation process. In order to solve the problem that the new measurement data is not available in case of GPS fault, an attitude algorithm based on Radial Basis Function (RBF)-ANN feedback correction is proposed for AUKF. The estimated deviation of predicted system state can be provided based on RBF-ANN in GPS-denied environment. The corrected predicted system state is used for the estimation process in AUKF. An experimental platform was setup to simulate the rotation of the spinning projectile. The experimental results show that the proposed method has better performance in terms of attitude estimation than other representative methods under various dynamic situations.

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“…Wei Wang and Zongyu Liu’s work was focused on the combination of GPS and INS navigation research, using the Kalman Filter (KF) and the Extended Kalman Filter (EKF) to compensate the error generated by the information fusion [13]. A decision tree-based multiple-model Unscented Kalman Filter (UKF) for attitude estimation using low-cost MEMS magnetic sensor arrays was proposed [14]. According to the theory provided, the effects of EKF and Unscented Kalman Filter (UKF) are almost the same [15]; however, UKF has a large number of calculations and a complicated algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on Low-Cost Attitude Estimation for MINS/Dual-Antenna GNSS Integrated Navigation Method

Wang

Liu

et al. 2019

Micromachines

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A high-precision navigation system is required for an unmanned vehicle, and the high-precision sensor is expensive. A low-cost, high-precision, dual-antenna Global Navigation Satellite System/Micro-electromechanical Systems-Inertial Navigation System (GNSS/MINS) combination method is proposed. The GNSS with dual antennas provides velocity, position, and attitude angle information as the measurement information is combined with the MINS. By increasing the heading angle, pitch angle, velocity, the accuracy of the integrated system is improved. The Extended Kalman Filtering (EKF) integrated algorithm simulation is designed to verify the feasibility and is realized based on the Field Programmable Gate Array and Advanced RISC Machine (ARM+FPGA) system. Static and dynamic tests were performed using the Synchronous Position, Attitude and Navigation (SPAN-CPT) as a reference system. The results show that the velocity, position, and attitude angle accuracy were improved. The yaw angle and pitch angle accuracy were 0.2° Root Mean Square (RMS) and 0.3° RMS, respectively. The method can be used as a navigation system for the unmanned vehicle.

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A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays

Cited by 29 publications

References 26 publications

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment

Research on Low-Cost Attitude Estimation for MINS/Dual-Antenna GNSS Integrated Navigation Method

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