Name
Abstract | ||
|---|---|---|
In this paper, we propose two improved particle filtering schemes for target tracking, one based on a gradient proposal and the other based on the turbo principle. We present the basic ideas and derivations and show detailed results of three tracking applications. Favorable experimental findings have shown the efficiency of our proposed schemes and their potential in other tracking scenarios. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415966 | ICASSP (4) |
Keywords | Field | DocType |
recursive bayesian estimation,kalman filters,bayesian bootstrap filter,tracking filters,sequential estimation,bayes methods,target tracking,turbo principle,gradient methods,monte carlo methods,gradient method,filtering schemes,sequential important sampling,recursive estimation,sequential monte carlo sampling,extended kalman filter,noise measurement,filtering,particle filter,signal processing,bayesian methods,particle filters | Turbo,Gradient method,Mathematical optimization,Extended Kalman filter,Noise measurement,Computer science,Particle filter,Filter (signal processing),Recursive Bayesian estimation,Kalman filter | Conference |
Volume | ISSN | ISBN |
4 | 1520-6149 | 0-7803-8874-7 |
Citations | PageRank | References |
2 | 0.38 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Chen | 1 | 2 | 0.38 |
| Thia Kirubarajan | 2 | 215 | 30.81 |
| Mark R. Morelande | 3 | 195 | 24.96 |