A Single Parameter Tunable Quaternion Based Attitude Estimation Filter

Bachmann, E.R.; Yun, Xiaoping

doi:10.1002/j.2161-4296.2006.tb00377.x

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…In [54], the filter was improved by introducing a more efficient computation of the correction step which made use of the orthogonality of the correction step to preserve the unity of the quaternion. Conditions for a tunable parameter was derived in [55] for the convergence of the filter within a reasonable time period as well as for the suppression of maneuver noise.…”

Section: Measurement Using Inertial and Magnetic Sensorsmentioning

confidence: 99%

Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network

Lee

Low

Taher

2010

IEEE Trans. Instrum. Meas.

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Measurement of human body motion has a myriad of applications ranging from gaming, rehabilitation, animation, virtual reality, sports science and surveillance. Existing methods of motion tracking include visual, mechanical, magnetic and inertial tracking. Visual methods require line of sight and suffer from the notorious occlusion problem. For the existing mechanical or inertial tracking methods, they have cumbersome wirings which hinder the natural movements. In this thesis, a wearable wireless sensor network using inertial/ magnetic sensors is developed to overcome the limitations of these existing methods. Using tri-axial accelerometer as the sole sensor will lead to singularity when the heading axis is vertical, restricting measurement of orientation to half a vertical plane. A new factorized quaternion approach is proposed in this research to overcome this deficiency with consideration of anatomical and sensor constraints. Different from the conventional approach based on single angle-axis quaternion, the proposed approach factorizes the quaternion into two principal axis quaternions corresponding to two equivalent arm motions. This allows for the implementation of anatomical arm constraints that match the range of arm motion and reduces the ambiguity in solutions. In addition, the singularities arising from the use of tri-axial accelerometers can be detected and resolved for a transient state. A novel algorithm based on elevation and heading angles is also proposed to determine the orientation of a sensor node equipped with tri-axial accelerometer, gyroscope and magnetometer. Compared to Euler angles, the fixed elevation and heading angles are independent on the temporal order of rotations. In addition, the fixed elevation and heading angles are observable and thus more intuitively visualized.

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Section: Measurement Using Inertial and Magnetic Sensorsmentioning

confidence: 99%

Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network

Lee

Low

Taher

2010

IEEE Trans. Instrum. Meas.

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“…Since both q and −q produce identical rotations, it is important to use both of these values in computing ∆q, which is the difference between the predicted and measured value for q that is used in drift correction, and to then choose the value for ∆q which is smaller in absolute value. This comparison is not necessary in some previous (less efficient and less accurate) approaches to drift correction [21] since these methods usually directly incorporate old estimates of q into estimation of ∆q, while this is not the case for the FQA estimate.…”

Section: Discussionmentioning

confidence: 99%

A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements

Yun

Bachmann

McGhee

2008

IEEE Trans. Instrum. Meas.

266

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Abstract-Orientation of a static or slow-moving rigid body can be determined from the measured gravity and local magnetic field vectors. Some formulation of the QUaternion ESTimator (QUEST) algorithm is commonly used to solve this problem. Triads of accelerometers and magnetometers are used to measure gravity and local magnetic field vectors in sensor coordinates. In the QUEST algorithm, local magnetic field measurements affect not only the estimation of yaw but also that of roll and pitch. Due to the deviations in the direction of the magnetic field vector between locations, it is not desirable to use magnetic data in calculations that are related to the determination of roll and pitch. This paper presents a geometrically intuitive 3-degree-of-freedom (3-DOF) orientation estimation algorithm with physical meaning [which is called the factored quaternion algorithm (FQA)], which restricts the use of magnetic data to the determination of the rotation about the vertical axis. The algorithm produces a quaternion output to represent the orientation. Through a derivation based on half-angle formulas and due to the use of quaternions, the computational cost of evaluating trigonometric functions is avoided. Experimental results demonstrate that the proposed algorithm has an overall accuracy that is essentially identical to that of the QUEST algorithm and is computationally more efficient. Additionally, magnetic variations cause only azimuth errors in FQA attitude estimation. A singularity avoidance method is introduced, which allows the algorithm to track through all orientations.

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“…(For more information please visit [11]). iv Psiaki [21], Um et al [23], Bachmann et al [27], Elkaim [29], Gebre-Egziabher et al [28], Shin et al [31], Tome et al [34], Hirokawa et al [35], Jun [36] use ⊗ to denote quaternion product or spectrum convolution Soloviev et al [32]. We believe it is incorrect to use ⊗ to denote quaternion product or multiplication or spectrum convolution as this symbol is used to denote Kronecker product [46] or Tensor product [12].…”

Section: Appendix D: Special Cases Of Quaternionic Analysismentioning

confidence: 98%

Quaternionic analysis of the influences of systematic biases on radar data processing

Chen

Wang

Progri³

2015

J. Geolo. Geo-inf. Geo-int.

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For single radar tracking, Kalman filter (KF) can be used only when radar measurements have Gaussian white noises. However, when systematic biases exist, application of KF for single radar tracking is not trivial. Furthermore, in the existing radar network, the association (both measurement-to-measurement and measurement-to-track) problem and the systematic biases removal are mixed together, which further complicates radar network data fusion. The key to solving these problems is to find and apply a set of mathematical tools (quaternionic analysis) on the biased measurements to directly enable the application of KF. This paper provides a detailed discussion of quaternionic analysis that can be applied to solve this problem. Measurement frame (MF) is proposed where target states contain radar systematic biases. It is proved that for stationary and first kind of mobile radar (installed on gyro-stabilized platform) target kinematic model (TKM) in MF has the same form with that in East-North-Up (ENU) frame. And KF can be used directly for the biased measurements to obtain the biased target track in the sense of minimum mean square error (MMSE). However, for the second kind of mobile radars (rigidly connected and sways with platform), a first-order approximation yields large errors therefore they (or second kind of mobile radars) cannot be applied well when attitude biases are greater than 1°. This situation will be investigated and discussed further in future publications.

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A Single Parameter Tunable Quaternion Based Attitude Estimation Filter

Cited by 3 publications

References 20 publications

Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network

Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network

A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements

Quaternionic analysis of the influences of systematic biases on radar data processing

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