Attitude Estimation from Vector Observations Using a Genetic-Algorithm-Embedded Quaternion Particle Filter

Oshman, Yaakov; Carmi, Avishy

doi:10.2514/1.17951

Cited by 78 publications

(86 citation statements)

References 10 publications

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“…The MPF was tested using a non-Gaussian TAM noise model, whose joint PDF may be double peaked distribution due to the additional magnetic dipole moment along the Y body frame axis which was encountered during the momentum wheel slow down [12]. The Y axis noise PDF of TAM is shown in Fig.…”

Section: Non-gaussian Measurement Noise Casementioning

confidence: 99%

Marginalized particle filter for spacecraft attitude estimation from vector measurements

Liu

Xue-yuan

2007

J. Control Theory Appl.

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An algorithm based on the marginalized particle filters (MPF) is given in details in this paper to solve the spacecraft attitude estimation problem: attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle in order to reduce the size of the state space and computational burden. The distribution of attitude vector is approximated by a set of particles and estimated using particle filter, while the estimation of gyro bias is obtained for each one of the attitude particles by applying the Kalman filter. The efficiency of this modified MPF estimator is verified through numerical simulation of a fully actuated rigid body. For comparison, unscented Kalman filter (UKF) is also used to gauge the performance of MPF. The results presented in this paper clearly demonstrate that the MPF is superior to UKF in coping with the nonlinear model.

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Section: Non-gaussian Measurement Noise Casementioning

confidence: 99%

Marginalized particle filter for spacecraft attitude estimation from vector measurements

Liu

Xue-yuan

2007

J. Control Theory Appl.

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“…2. The lower bounds on both the attitude and the feature point channels are numerically approximated for the problem at hand using the techniques described in [33] and [14]. Fig.…”

Section: A Crlb Computationmentioning

confidence: 99%

Stereovision-Based Estimation of Relative Dynamics Between Noncooperative Satellites: Theory and Experiments

Segal

Carmi

Gurfil

2014

IEEE Trans. Contr. Syst. Technol.

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152

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Estimating the relative pose and motion of cooperative satellites using on-board sensors is a challenging problem. When the satellites are noncooperative, the problem becomes even more complicated, as there might be poor a priori information about the motion and structure of the target satellite. In this paper, the mentioned problem is solved by using only visual sensors, which measurements are processed through robust filtering algorithms. Using two cameras mounted on a chaser satellite, the relative state with respect to a target satellite, including the position, attitude, and rotational and translational velocities, is estimated. The new approach employs a stereoscopic vision system for tracking a set of feature points on the target spacecraft. The perspective projection of these points on the two cameras constitutes the observation model of an iterated extended Kalman filter (IEKF) estimation scheme. Using new theoretical results, the information contained in the visual data is quantified using the Fisher information matrix. It is shown that, even in the noncooperative case, there is information that can be extracted pertaining to the relative attitude and target structure. Finally, a method is proposed for rendering the relative motion filtering algorithm robust to uncertainties in the target's inertia tensor. This is accomplished by endowing the IEKF with a maximum a posteriori identification scheme for determining the most probable inertia tensor from several available hypotheses. The performance of the new filtering algorithm is validated by Monte-Carlo simulations. Also a preliminary experimental evaluation is provided.

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“…Since 1965, a multitude of solutions have been proposed to resolve attitude estimation problem, such as TRIAD [21], QUaternion ESTimator (QUEST) [22], Singular Value decomposition method (SVD) [23], Kalman Filters (KF) [24,25,26,27,28], Extended Kalman Filters (EKF) [29,30,31,32,5], Unscented Kalman Filters (UKF) [33], Adaptive Kalman Filters (AKF) [34,35], Particle Filters [36] and more recently Observers [12,37,38,39]. A survey and an analysis of these methods can be found in [40].…”

Section: Related Workmentioning

confidence: 99%

Attitude estimation for indoor navigation and augmented reality with smartphones

Michel

Genevès

Fourati

et al. 2018

Pervasive and Mobile Computing

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We investigate the precision of attitude estimation algorithms in the particular context of pedestrian navigation with commodity smartphones and their inertial/magnetic sensors. We report on an extensive comparison and experimental analysis of existing algorithms. We focus on typical motions of smartphones when carried by pedestrians. We use a precise ground truth obtained from a motion capture system. We test state-of-the-art and built-in attitude estimation techniques with several smartphones, in the presence of magnetic perturbations typically found in buildings. We discuss the obtained results, analyze advantages and limits of current technologies for attitude estimation in this context. Furthermore, we propose a new technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We show how our technique compares and improves over previous works. A particular attention was paid to the study of attitude estimation in the context of augmented reality motions when using smartphones.

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Attitude Estimation from Vector Observations Using a Genetic-Algorithm-Embedded Quaternion Particle Filter

Cited by 78 publications

References 10 publications

Marginalized particle filter for spacecraft attitude estimation from vector measurements

Marginalized particle filter for spacecraft attitude estimation from vector measurements

Stereovision-Based Estimation of Relative Dynamics Between Noncooperative Satellites: Theory and Experiments

Attitude estimation for indoor navigation and augmented reality with smartphones

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