Attitude and Heading Reference System for Quadrotor Based on MEMS Sensors

Jiang, Qiang; Zeng, Yong; Liu, Qiang; Hua, Jing

doi:10.1109/imccc.2012.257

Cited by 7 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ector in the local period. 5) at the integraion e direction cosine navigation frame tion interval and he white noise and an be effectively hen calculates the or as shown in (7). on each sensor's tion, yet it can be te (0.5Hz) during IMU data are g and the at, without sensor = [0, 0, -g].…”

Section: Model Formulation Via Velocity Increment Vectormentioning

confidence: 99%

“…Specifically, the random bias error behaves as a principal factor for attitude divergence. To correct this, the gravity vector measured by accelerometer can be used, which is free from a divergence property [3][4][5]. Yet during a translational motion, the accelerometer measures specific force (gravity and dynamic forces) simultaneously, thus it may be hard to estimate true roll and pitch orientation through rate integration of acceleration measurements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

Lee¹,

Yun²,

Lee³

et al. 2016

International Journal of Aeronautical and Space Sciences

View full text Add to dashboard Cite

This paper proposes a new method achieving computationally efficient attitude reference system for low cost strapdown sensors and microprocessor platform. The main idea in this method is to define and compare velocity differential vectors, geometrically computed from INS and GPS data with different update rate, for generating attitude error measurements which is further used for filter construction. A quaternion based Kalman filter configuration is applied for the attitude estimation with the adapted measurement model of differential vector comparison. Linearized model for Extended Kalman Filter and low pass filtered characteristics of measurement greatly extend the affordability of the proposed algorithm to the field of simple low cost embedded systems. For performance verification, experiment are done employing a practical low cost MEMS IMU and GPS receiver specification. Performance comparison with a high grade navigation system demonstrated good estimation result.

show abstract

Section: Model Formulation Via Velocity Increment Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

Lee¹,

Yun²,

Lee³

et al. 2016

International Journal of Aeronautical and Space Sciences

View full text Add to dashboard Cite

show abstract

“…A proportion of the residual determined by the gain factor compensates the estimated gyroscope error in application dynamically. Roll opt /Yaw opt = Roll Gyro /Yaw Gyro (4) If Yaw Error /Roll Error ≈ 180 ∘ (5) Then (6) Yaw opt /Roll opt = Yaw Gyro /Roll Gyro (7) Otherwise (8) If Yaw Gyro /Roll Gyro > 0 (9) Then (10) Yaw opt /Roll opt = Yaw Gyro /Roll Gyro − 180 ∘ (11) Otherwise (12) Yaw opt /Roll opt = Yaw Gyro /Roll Gyro + 180 ∘ (13) End if (14) End if (15)…”

Section: Calibration Models Of Mems Sensorsmentioning

confidence: 99%

“…Due to restrictions from expensiveness, complexity, weight, and large space consumption, traditional gimbaled inertial navigation system based on mechanical gyroscopic instruments does not always fit best [5,6]. Meanwhile, with progress of microelectromechanical system (MEMS) technology, AHRS built with low-cost MEMS gyroscopes, accelerometers, and magnetometers [7][8][9][10] have attracted attention of engineers and are replacing traditional attitude estimation systems in many applications as they come more economical, compact, and convenient [5,11,12].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering

Wang

Ning

Chen

et al. 2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This paper presents design and implementation of an attitude and heading reference system (AHRS) based on low-cost MEMS sensors and complementary filtering (CF). Different from traditional solutions, information fusion is performed with Euler angles directly, which is more straightforward for understanding; however it proposes many challenges for reaching a stable and accurate estimation as when these angles approach or traverse their range boundaries, estimation may get discontinuous. Thus an effective discontinuity avoiding strategy is suggested in this paper to refine the estimation. Besides, instead of extended Kalman filtering (EKF), CF is utilized for state estimation of AHRS as it features fusion of high-frequency and low-frequency signals. In order to make up for shortcomings of MEMS sensors such as multiple errors, drifts, and bad accuracy, some effective calibration and filtering algorithms are proposed to guarantee agreeable AHRS performance. Also, architecture of the MEMS IMU (inertial measurement unit) and mathematical principles for AHRS solution are explained and implemented in this paper. Meanwhile, experimental comparisons have proved feasibility and acceptable performance of this AHRS design.

show abstract

FPGA Implementation of a Interval Type-2 Fuzzy Inference for Quadrotor Attitude Control

Matusi

Oda

Yukawa

et al. 2022

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Attitude and Heading Reference System for Quadrotor Based on MEMS Sensors

Cited by 7 publications

References 2 publications

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

An Efficient Attitude Reference System Design Using Velocity Differential Vectors under Weak Acceleration Dynamics

Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering

FPGA Implementation of a Interval Type-2 Fuzzy Inference for Quadrotor Attitude Control

Contact Info

Product

Resources

About