Imperial College London
Alternate

Dr Imad M. Jaimoukha

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6279i.jaimouka Website

 
 
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Location

 

617Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ahmed:2012:10.1109/TSP.2012.2198820,
author = {Ahmed, S and Kerrigan, EC and Jaimoukha, IJ and Ahmed, S and Kerrigan, EC and Jaimoukha, IM},
doi = {10.1109/TSP.2012.2198820},
journal = {IEEE Transactions on Signal Processing},
pages = {3942--3952},
title = {A Semidefinite Relaxation-Based Algorithm for Robust Attitude Estimation},
url = {http://dx.doi.org/10.1109/TSP.2012.2198820},
volume = {60},
year = {2012}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper presents a tractable method for solving a robust attitude estimation problem, based on a weighted least squares approach with nonlinear constraints. Attitude estimation requires information of a few vector quantities, each obtained from both a sensor and a mathematical model. By considering the modeling errors, measurementnoise, sensor biases and offsets as infinity-norm bounded uncertainties, we formulate a robust optimization problem, which is non-convex with nonlinear cost and constraints. The robust min-max problem is approximated with a non-convex minimization problem using an upper bound. A new regularization scheme is also proposed to improve the robust performance. We then use semidefinite relaxation to convert the suboptimal problem with quadratic cost and constraints into a tractable semidefinite program with a linear objective function and linear matrix inequality constraints. We also show how to extract the solution of the suboptimal robust estimation problem from the solution of the semidefinite relaxation. Further, a mathematical proof supported by numerical results is presented stating the gap between the suboptimal problem and its relaxation is zero under a given condition, which is mostly true in real life scenarios. The usefulness of the proposed algorithm in the presence of uncertainties is evaluated with the helpof examples.
AU  - Ahmed,S
AU  - Kerrigan,EC
AU  - Jaimoukha,IJ
AU  - Ahmed,S
AU  - Kerrigan,EC
AU  - Jaimoukha,IM
DO  - 10.1109/TSP.2012.2198820
EP  - 3952
PY  - 2012///
SN  - 1053-587X
SP  - 3942
TI  - A Semidefinite Relaxation-Based Algorithm for Robust Attitude Estimation
T2  - IEEE Transactions on Signal Processing
UR  - http://dx.doi.org/10.1109/TSP.2012.2198820
UR  - http://hdl.handle.net/10044/1/10012
VL  - 60
ER  -
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