Name
Abstract | ||
|---|---|---|
The performance of Global Satellite Positioning System / Inertial Navigation System (GPS/INS) integrated navigation system based on Kalman Filter (KF) is greatly influenced by measurement information related to GPS. However, it can be unreliable: it can be lost and the statistical characteristics of the measurement noise can change. Thus, the performance of navigation will get worse. Therefore, a filter algorithm for the integrated navigation system based on the Interacting Multiple Model-Adaptive Filter (IMM-AF) is proposed in this paper. Two measurement noise models for small Gaussian noise and non-small Gaussian noise are designed respectively to be applied to the algorithm; one step prediction algorithm for the case of GPS signal loss is also combined. The results of the experiment of the integrated navigation system of mobile robot show that, compared with KF or IMM, IMM-AF algorithm presents higher accuracy and better robustness, with almost the same update time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729212 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
GPS/INS, measurement information, non-Gaussian noise, models, IMM-AF | Inertial navigation system,Computer vision,Extended Kalman filter,Computer science,Navigation system,GPS/INS,Kalman filter,Artificial intelligence,Global Positioning System,GPS signals,Gaussian noise | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhilu Wu | 1 | 184 | 29.47 |
| Yuyuan Zhang | 2 | 0 | 0.34 |
| Jinlong Sun | 3 | 0 | 0.34 |
| Zhendong Yin | 4 | 67 | 16.12 |