2021
DOI: 10.1142/s0218488521400092
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Design and Implementation of Attitude and Heading Reference System with Extended Kalman Filter Based on MEMS Multi-Sensor Fusion

Abstract: The accuracy of attitude and heading measurement, as well as the system real-time performance are basic indicators used to evaluate an attitude and heading reference system (AHRS). In order to improve the attitude and heading measurement accuracy under dynamic complex environment, the AHRS system should also have numerical stability and calculation robustness. The AHRS system based on MEMS multi-sensor fusion can realize fusion processing of data measured by multiple sensors, so as to calculate and obtain the … Show more

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Cited by 12 publications
(9 citation statements)
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“…It has the characteristics of complex human structure, rich details, unclear edge, and huge amount of data. Many traditional segmentation methods cannot obtain good segmentation results for virtual human image [6]. erefore, at present, the segmentation of virtual human image is mainly manual segmentation, supplemented by interactive segmentation methods [7].…”
Section: Literature Reviewmentioning
confidence: 99%
“…It has the characteristics of complex human structure, rich details, unclear edge, and huge amount of data. Many traditional segmentation methods cannot obtain good segmentation results for virtual human image [6]. erefore, at present, the segmentation of virtual human image is mainly manual segmentation, supplemented by interactive segmentation methods [7].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Reference [36] 0.916 Reference [37] 0.976 Reference [38] 0.881 Reference [39] 0.743 Reference [40] 0.885 Our 0.989 10 Advances in Mathematical Physics…”
Section: Methods Accuracymentioning
confidence: 99%
“…The vector representation of, and over time, this state will change and be mixed with noise and additional control information. Therefore, in order to obtain real data from noisy observation data, the Kalman filter must be modelled and defined, as shown in Figure 16 [10][11][12]. The Kalman filter model in Figure 3, circles represent vectors, squares represent matrices, and asterisks represent Gaussian noise.…”
Section: Quadcopter System Architecturementioning
confidence: 99%