Frequency-weighted state estimation with application to estimation in the presence of sensor bias

Alder, Lawrence J.; Rock, Stephen M.

doi:10.1109/87.508891

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Coordinate transformations A transformation of the point coordinates from one Cartesian coordinate system to another is in general given by Ps = Rs (pn + r,s) = Rsp n +rns (1) where pn and p5 are coordinate vectors written in Fn and ¶5 respectively, Rn is a coordinate transformation matrix and r and rs are translation vectors from ¶Fn to ¶s, written in ¶Fn and ¶s respectively. A composite transformation is represented by the product of two transformation matrices.…”

Section: Coordinateframesmentioning

confidence: 99%

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Bias Estimation for Distributed Radars in 3D

Topland

Hallingstad

Sinha

et al. 2007

2007 IEEE Aerospace Conference

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An important property of a multisensor data fusion scheme in a target tracking scenario is to be able to improve tracking by adding more sensors. This property can be difficult to implement because of unknown sensor measurement biases. Not accounting for these may lead to more false tracks and poor performance. It is desirable to find all of these biases from measurements from common targets. We consider a network of 3D sensors observing targets which are moving at constant speed, heading and altitude. The measurements are subject to biases due to uncertain sensor alignment and location, and biases from sensor imperfections. The observability of these biases are discussed, and most importantly it is shown that absolute, as opposed to relative, alignment biases can be estimated when taking into account the curvature of the Earth. Simulations show that the presented model is not satisfactory to estimate the biases, and a new model is proposed.

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Section: Coordinateframesmentioning

confidence: 99%

“…In [1] bias estimation of a single sensor is done seen from a frequency analysis point of view. A modified linear quadratic Gaussian (LQG) estimator eliminates the sensor bias.…”

Section: Introductionmentioning

confidence: 99%

Bias Estimation for Distributed Radars in 3D

Topland

Hallingstad

Sinha

et al. 2007

2007 IEEE Aerospace Conference

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“…Alder and Rock (1994) used a linear Kalman filter and a state feedback control law in conjunction with an adaptive control algorithm to control the end-effector position of a planar one-link flexible robot arm with unknown payload dynamics. The same authors (Alder and Rock 1996) used a linear Kalman filter to estimate the state of a planar one-link flexible robot arm in the presence of permanent link deformations. Their Kalman filters used measurements of end-effector position and measurements of joint angular rates.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filtering Applied to a Two-Axis Robotic Arm with Flexible Links

Lertpiriyasuwat

Berg

Buffinton

2000

The International Journal of Robotics Research

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An industrial robot today uses measurements of its joint positions and models of its kinematics and dynamics to estimate and control its end-effector position. Substantially better end-effector position estimation and control performance would be obtainable if direct measurements of its end-effector position were also used. The subject of this paper is extended Kalman filtering for precise estimation of the position of the end-effector of a robot using, in addition to the usual measurements of the joint positions, direct measurements of the end-effector position. The estimation performances of extended Kalman filters are compared in applications to a planar two-axis robotic arm with very flexible links. The comparisons shed new light on the dependence of extended Kalman filter estimation performance on the quality of the model of the arm dynamics that the extended Kalman filter operates with.

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“…One commonly used observer is the linear Kalman filter (KF) or the extended Kalman filter (EKF), used for nonlinear systems. A KF is used for a single-axis robot arm in [5] and [6]. EKFs are used in [7], and also in [8], where a two-axis robot, with tool position and joint speed measurements, are used.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Six Different Sensor Fusion Methods for an Industrial Robot using Experimental Data

Axelsson

2012

IFAC Proceedings Volumes

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This paper summarizes previous work on tool position estimation on industrial manipulators, and emphasize the problems that must be taken care of in order to get a satisfied result. The acceleration of the robot tool, measured by an accelerometer, togheter with measurements of motor angles are used. The states are estimated with an extended kalman filter. A method for tuning the covariance matrices for the noise, used in the observer, is suggested. The work has been focused on a robot with two degrees of freedom.

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Frequency-weighted state estimation with application to estimation in the presence of sensor bias

Cited by 5 publications

References 4 publications

Bias Estimation for Distributed Radars in 3D

Bias Estimation for Distributed Radars in 3D

Extended Kalman Filtering Applied to a Two-Axis Robotic Arm with Flexible Links

Evaluation of Six Different Sensor Fusion Methods for an Industrial Robot using Experimental Data

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