Name
Abstract | ||
|---|---|---|
A new tracking algorithm is proposed. It treats the target acceleration as a nonrandom term, and consists of a constant velocity filter an input estimator and a maneuver detector implemented in parallel. The new method has the same advantages as the two-stage Kalman estimator which requires a lesser amount of computation and provides even a better performance when compared with an augmented state Kalman filter. At the same time, the new method uses a better tuning parameter and removes a difficulty in implementation of the two-stage Kalman estimator. It is shown that the new filter is a better alternative to the two-stage Kalman estimator on tracking maneuvering targets. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ACC.2002.1024798 | American Control Conference, 2002. Proceedings of the 2002 |
Keywords | DocType | Volume |
kalman filters,filtering theory,state estimation,target tracking,augmented state kalman filter,constant velocity filter,input estimation,maneuver detector,maneuvering target tracking algorithm,nonrandom term,target acceleration,tuning parameter,two-stage kalman estimator,tracking systems,convergence,kalman filter,acceleration,mechanical engineering,white noise,algorithms,kalman filtering,estimating,detectors,linear systems | Conference | 1 |
ISSN | Citations | PageRank |
0743-1619 | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhou | 1 | 7 | 1.77 |
| Xiqin Wang | 2 | 290 | 33.88 |
| M. Tomizuka | 3 | 1464 | 294.37 |
| Wei-Bin Zhang | 4 | 159 | 30.80 |
| Ching-Yao Chan | 5 | 79 | 23.48 |