Name
Abstract | ||
|---|---|---|
The fitting of a collection of noisy data points to a circle is a nonlinear and challenging problem, and it plays an important role in many signal processing applications. This paper proposes a semi-definite programming solution for the circle fitting problem based on the semi-definite relaxation technique. The relaxation of the maximum likelihood estimation converts a nonconvex problem to an approximate but convex one that can be solved by using the semi-definite programming method. The performance of the proposed solution is examined via simulations and compared with the Kasa method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISCAS.2012.6272003 | ISCAS |
Keywords | Field | DocType |
circle fitting,signal processing,maximum likelihood estimation,noisy data points,convex optimisation,convex programming,nonconvex problem,semidefinite relaxation technique,signal processing applications,semidefinite programming,data handling,kasa method,noise,accuracy,programming,noise measurement | Signal processing,Applied mathematics,Mathematical optimization,Nonlinear system,Computer science,Control theory,Regular polygon,Relaxation technique,Estimation theory,Maximum likelihood sequence estimation,Convex optimization,Semidefinite programming | Conference |
Volume | Issue | ISSN |
null | null | 0271-4302 |
ISBN | Citations | PageRank |
978-1-4673-0218-0 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhua Ma | 1 | 19 | 2.20 |
| Le Yang | 2 | 273 | 33.24 |
| K.C. Ho | 3 | 1311 | 148.28 |