scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

A smoother for attitude estimation using inertial and magnetic sensors

01 Nov 2010-pp 743-746
TL;DR: A smoother is proposed for an attitude estimation problem, where attitude is estimated using inertial and magnetic sensors and the final attitude quaternion is computed using the averaging algorithm of quaternions, which can be computed from a simple optimization problem.
Abstract: A smoother is proposed for an attitude estimation problem, where attitude is estimated using inertial and magnetic sensors. A smoother can provide better results when an off-line processing is allowed. A quaternion is used to represent attitude. An indirect Kalman filter is used as a forward and backward filter. In the smoother, the final attitude quaternion is computed using the averaging algorithm of quaternions, which can be computed from a simple optimization problem.
Citations
More filters
Journal ArticleDOI
07 Feb 2012-Sensors
TL;DR: Through experiments, it is shown that the proposed gait analysis system can both track foot motion and estimate step length.
Abstract: In this paper, a gait analysis system which estimates step length and foot angles is proposed. A measurement unit, which consists of a camera and inertial sensors, is installed on a shoe. When the foot touches the floor, markers are recognized by the camera to obtain the current position and attitude. A simple planar marker with 4,096 different codes is used. These markers printed on paper are placed on the floor. When the foot is moving off the floor, the position and attitude are estimated using an inertial navigation algorithm. For accurate estimation, a smoother is proposed, where vision information and inertial sensor data are combined. Through experiments, it is shown that the proposed system can both track foot motion and estimate step length.

41 citations


Cites methods from "A smoother for attitude estimation ..."

  • ...A smoothing algorithm [13] consisting of a forward filter and a backward filter is used....

    [...]

Journal ArticleDOI
TL;DR: In this article, the attitude and position estimation of a short moving interval between two zero velocity intervals is studied. But the proposed method is less sensitive to the uncalibrated sensor parameters and sensor noises, which are main sources of estimation errors.
Abstract: This paper is concerned with attitude and position estimation of movement, which is between two zero velocity intervals. The proposed method is suitable for analysis of movement for a short moving interval. Using two boundary information (zero velocity interval), attitude is estimated by an attitude smoother. Using the smoothed attitude, velocity is estimated by a velocity smoother. The position is computed by integrating the velocity estimate. Through numerical examples, the proposed method is shown to be less sensitive to the uncalibrated sensor parameters and sensor noises, which are main sources of estimation errors.

25 citations


Cites methods from "A smoother for attitude estimation ..."

  • ...We note that forward and backward smoothers were also used in [16] and [17]....

    [...]

Journal ArticleDOI
TL;DR: Six competitive methods are compared with the method, and simulation and experiment tests validate the superiority of the method.
Abstract: Magnetic and inertial measurement units (MIMUs) are promising tools for attitude tracking of moving bodies without location restriction. An extended Kalman filter (EKF) is a commonly used attitude algorithm for MIMUs, and its Kalman gain is usually regulated according to the measurements of the accelerometer for the best integrated performance, i.e., the best performance for both when the carrier is motionless and when the carrier is moving. A hidden Markov model (HMM) is introduced, and then trained using static measurements of the accelerometer. Once the body has a movement, the match probability between the dynamic measurements of the accelerometer and the trained HMM will decrease, which is then used for the timely regulation of the Kalman gain to make the EKF rely more on the measurements of the gyroscope for attitude calculation. A slight revision to the introduced HMM is given for the improvement of the smoothness of the outputs of the HMM when the carrier is motionless. A relationship between the Kalman gain and the output of the HMM is also given, and the value range of the outputs of the HMM is readjusted in order to fit that relationship. Six competitive methods are compared with our method, and simulation and experiment tests validate the superiority of our method.

10 citations


Cites background from "A smoother for attitude estimation ..."

  • ...[18] set T0 = 0, however, the values of fs and α are not given....

    [...]

Journal ArticleDOI
TL;DR: A new attitude algorithm for MIMU known as REQUEST is introduced, and an artificial neural network (ANN) is used to establish the functional relationship between this representation and the weights assigned to the vector measurements in REQUEST, for the purpose of adaptively regulating the vectors measurements according to the representation.
Abstract: Magnetic and inertial measurement units (MIMU) are currently being explored as a promising tool for attitude tracking of a moving object, such as human body parts. The function of attitude calculation is realized by using attitude algorithms. The overall performance of these algorithms is seriously influenced by linear acceleration of the moving object. Therefore, there is a requirement to find solutions to this problem. In this paper, a new attitude algorithm for MIMU known as REQUEST is introduced. The algorithm is then revised in order to be suitable for MIMU. A new representation of linear acceleration of the moving object is then constructed. An artificial neural network (ANN) is used to establish the functional relationship between this representation and the weights assigned to the vector measurements in REQUEST, for the purpose of adaptively regulating the vector measurements according to the representation. In this way, the measurements of the gyroscope can be relied on more for attitude calculation when the carrier has a higher linear acceleration. A Kalman filter (KF) is also used prior to the establishment of the functional relationship in order to take full advantage of historical sensor information for accurate estimation of the representation. Our experiments have verified good static and dynamic performances of our KF+ANN-based REQUEST algorithm.

10 citations

Journal ArticleDOI
TL;DR: In this article, a foot motion is estimated using an inertial sensor unit, which is installed on a shoe, which consists of 3 axis accelerometer and 3 axis gyroscopes.
Abstract: A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.

1 citations


Additional excerpts

  • ...[9]에서는 자세 추정을 위한 평활기가 제시되었고, [10]에서는 외부 위치 센서를 사용한 경우에 자세 및 위치 추정을 위한 평활기가 제시되었고, [11]에서는 자세 평활기 를 구한 후 다시 속도 평활기를 구하는 방법이 제안되었다....

    [...]

References
More filters
MonographDOI
01 Jan 1999
TL;DR: In this article, J.B. Kuipers introduces quaternions for scientists and engineers who have not encountered them before and shows how they can be used in a variety of practical situations.
Abstract: Ever since the Irish mathematician William Rowan Hamilton introduced quaternions in the 19th century - a feat he celebrated by carving the founding equations into a stone bridge - mathematicians and engineers have been fascinated by these mathematical objects. They are used in applications as various as describing the geometry of space-time, guiding the Space Shuttle, and developing computer applications in virtual reality. In this book, J.B. Kuipers introduces quaternions for scientists and engineers who have not encountered them before and shows how they can be used in a variety of practical situations.

1,062 citations


"A smoother for attitude estimation ..." refers methods in this paper

  • ...SMOOTHER A quaternion q ∈ R(4) is used to represent attitude [6]....

    [...]

Journal ArticleDOI
TL;DR: The physical principles underlying the variety of approaches to motion tracking are introduced and it is shown that certain methods work quite well for specific applications.
Abstract: This article introduces the physical principles underlying the variety of approaches to motion tracking. Although no single technology will work for all purposes, certain methods work quite well for specific applications.

680 citations


"A smoother for attitude estimation ..." refers background in this paper

  • ...INTRODUCTION Attitude estimation using inertial sensors and magnetic sensors is used in many applications [1] such as human motion tracking, unmanned aerial vehicle, and augmented reality....

    [...]

Journal ArticleDOI
TL;DR: This paper is concerned with orientation estimation using inertial and magnetic sensors using quaternion-based indirect Kalman filter structure and the proposed method prevents unnecessarily increasing the measurement noise covariance corresponding to the accelerometer output, which is not affected by external acceleration.
Abstract: This paper is concerned with orientation estimation using inertial and magnetic sensors. A quaternion-based indirect Kalman filter structure is used. The magnetic sensor output is only used for yaw angle estimation using two-step measurement updates. External acceleration is estimated from the residual of the filter and compensated by increasing the measurement noise covariance. Using the direction information of external information, the proposed method prevents unnecessarily increasing the measurement noise covariance corresponding to the accelerometer output, which is not affected by external acceleration. Through numerical examples, the proposed method is verified.

220 citations


"A smoother for attitude estimation ..." refers background or methods in this paper

  • ...The forward filter structure is essentially the same as that in [7] except the adaptive algorithm part (?̂?ab,k in (10))....

    [...]

  • ...For a forward filter, a slightly modified algorithm in [7] is used....

    [...]

Book
26 Jul 2007
TL;DR: Vectors and Matrices Coordinate Transformation between Orthonormal Frames Forms of the Transformation Matrix Earth and Navigation and Equations Implementation.
Abstract: Vectors and Matrices Coordinate Transformation between Orthonormal Frames Forms of the Transformation Matrix Earth and Navigation The Inertial Navigation System Equations Implementation Air Data Computer Polar Navigation Alignment Attitude and Heading Reference System Inertially Aided System Appendices.

146 citations


"A smoother for attitude estimation ..." refers background in this paper

  • ...The derivative of q is given by the following equation [8]:...

    [...]

Book
02 Jan 2013
TL;DR: The importance and role of avionics in the avionic environment is highlighted, as well as the importance of unmanned air vehicles, in the context of commercial off-the-shelf (COTS).
Abstract: Foreword. Preface. Acknowledgements. 1: Introduction. 1.1. Importance and role of avionics. 1.2. The avionic environment. 1.3. Choice of units. 2: Displays and man-machine interaction. 2.1. Introduction. 2.2. aHead up displays. 2.3. Helmet mounted displays. 2.4. Computer aided optical design. 2.5. Discussion of HUDs vs HMDs. 2.6. Head down displays. 2.7. Data fusion. 2.8. Intelligent displays management. 2.9. Displays technology. 2.10. Control and data entry. Further reading. 3: Aerodynamics and aircraft control. 3.1. Introduction. 3.2. aBasic aerodynamics. 3.3. Aircraft stability. 3.4. Aircraft dynamics. 3.5. Longitudinal control and response. 3.6. Lateral control. 3.7. Powered flying controls. 3.8. Auto-stabilisation systems. Further reading. 4: Fly-by-wire flight control. 4.1. Introduction. 4.2. aFly-by-wire flight control features and advantages. 4.3. Control laws. 4.4. Redundancy and failure survival. 4.5. Digital implementation. 4.6. Fly-by-light flight control. Further reading. 5: Inertial sensors and attitude derivation. 5.1. Introduction. 5.2. Gyros and accelerometers. 5.3. Attitude derivation. Further reading. 6: Navigation systems. 6.1. Introduction and basic principles. 6.2. Inertial navigation. 6.3. Aided IN systems and Kalman filters. 6.4. Attitude and heading reference systems. 6.5. GPS - global positioning systems. 6.6. Terrain reference navigation. Further reading. 7: Air data and air data systems. 7.1. Introduction. 7.2. Air data information and its use. 7.3. Derivation of air data laws and relationships. 7.4. Air data sensors and computing. Further reading. 8: Autopilots and flight management systems. 8.1. Introduction. 8.2. Autopilots. 8.3. Flight management systems. Further reading. 9: Avionic systems integration. 9.1. Introduction and background. 9.2. Data bus systems. 9.3. Integrated modular avionics. 9.4. Commercial off-the-shelf (COTS). Further reading. 10: Unmanned air vehicles. 10.1. Importance of unmanned air vehicles. 10.2. UAV avionics. Further reading. Glossary of terms. List of symbols. List of abbreviations. Index.

145 citations