Attitude Measurement for High-Spinning Projectile with a Hollow MEMS IMU Consisting of Multiple Accelerometers and Gyros

Liu, Fu-chao; Su, Zhong; Zhao, Hui; Li, Qing; Li, Chao

doi:10.3390/s19081799

Cited by 19 publications

(10 citation statements)

References 41 publications

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“…Inspired by the success of redundant integrated sensors for processing high-dimensional sensor data [24], [26], [38], and the benefits of redundant, non-orthogonal sensor structures [38]- [41], we propose a skewed-redundant magnetometer platform for perturbation-free heading estimation. The approach adopted in this paper relies on the use of redundant sensor information to enhance the overall performance of the heading estimation while at the same time ensuring the integrity of the system.…”

Section: Introductionmentioning

confidence: 99%

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Karimi

Babaians

Oelsch

et al. 2020

2020 Fourth IEEE International Conference on Robotic Computing (IRC)

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Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewedredundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbationfree Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74°for indoor heading estimation without using an additional gyroscope.*Preprint Version-Accepted article in the 4th IEEE International Conference on Robotic Computing.

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Section: Introductionmentioning

confidence: 99%

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Karimi

Babaians

Oelsch

et al. 2020

2020 Fourth IEEE International Conference on Robotic Computing (IRC)

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“…Due to these characteristics of the projectile working environment, the attitude measurement method should meet the requirements of high accuracy, anti-overload, high output rate, and small in occupation. Therefore, the accurate measurement of projectile attitude as the basis of precise guidance and control has been regarded as a difficult task for a long time [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System

Zhang

et al. 2020

Micromachines

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Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

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“…MEMS Inertial Measurement Unit (MIMU) and magnetic sensor have wide application for attitude determination in civilian and military fields, such as Unmanned Aerial Vehicle (UAV) systems [1,2], land vehicle systems [3,4], projectile, spinning shell [5,6], underwater system [7,8], and others [9,10]. In these specific applications, the work of different attitude determination algorithms is classified according to the following categories.…”

Section: Introductionmentioning

confidence: 99%

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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Attitude Measurement for High-Spinning Projectile with a Hollow MEMS IMU Consisting of Multiple Accelerometers and Gyros

Cited by 19 publications

References 41 publications

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System

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

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