Name
Title | ||
|---|---|---|
Optimal Gaussian filtering for polynomial systems applied to association-free multi-target tracking |
Abstract | ||
|---|---|---|
This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typcial multiple target tracking scenarios. |
Year | Venue | Keywords |
|---|---|---|
2011 | Information Fusion | Gaussian noise,polynomials,state estimation,target tracking,tracking filters,Gaussian noise,SME filter,analytic moment calculation,data association uncertainty,discrete-time multidimensional polynomial,multitarget tracking,nonlinear state estimation problem,optimal Gaussian filtering,symmetric measurement equation filter,symmetric polynomials,Gaussian filtering,SME filter,multi-target tracking,polynomial systems |
Field | DocType | ISBN |
Symmetric function,Polynomial,Control theory,Computer science,Artificial intelligence,Symmetric polynomial,Gaussian filter,Computer vision,Algorithm,Filter (signal processing),Kalman filter,Gaussian,Gaussian noise | Conference | 978-1-4577-0267-9 |
Citations | PageRank | References |
4 | 0.53 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baum, M. | 1 | 4 | 0.53 |
| Benjamin Noack | 2 | 168 | 23.73 |
| Beutler, F. | 3 | 4 | 0.53 |
| Itte, D. | 4 | 4 | 0.53 |
| Hanebeck | 5 | 4 | 0.53 |