Name
Abstract | ||
|---|---|---|
The accuracy of Global Positioning System (GPS) is often combined with the reliability of Inertial Navigation System (INS) to accomplish navigation. This paper proposes an innovative way to filter and fuse the GPS and INS information. UKF is employed to simulate the information convergence of the dynamic model which maintains better performance in nonlinear system. So we can obtain a fair precise filtering result when both are online. At the same time, the INS data is trained with the result as training target when it is the unique input. This paper raises the idea that Support Vector Machine (SVM) is adopted to train the INS data during GPS outage and the simulated annealing is applied to realize the optimization of the parameters of kernel function and the penalty function in the SVM algorithm. Therefore, the integration navigation could retain almost as precise as the GPS when the GPS is off-line. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/VTCFall.2013.6692217 | VTC Fall |
Keywords | Field | DocType |
global positioning system,gps-ins integrated navigation,svm,fair precise filtering,ukf,support vector machine,nonlinear system,navigation,reliability,inertial navigation system,filtering theory,gps/ins,simulated annealing,inertial navigation,support vector machines,kernel function | Inertial navigation system,Computer science,GPS/INS,Electronic engineering,Real-time computing,Global Positioning System,Artificial intelligence,Precise Point Positioning,Simulated annealing,Computer vision,Support vector machine,Filter (signal processing),Kernel (statistics) | Conference |
Volume | Issue | ISSN |
null | null | 1090-3038 |
Citations | PageRank | References |
2 | 0.37 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuqing Jiang | 1 | 33 | 18.70 |
| Chonghua Liu | 2 | 8 | 2.52 |
| Gong Zhang | 3 | 2 | 0.37 |
| Yupeng Wang | 4 | 10 | 2.32 |
| Cheng-Kai Huang | 5 | 21 | 3.38 |
| Jiayi Liang | 6 | 2 | 0.37 |