Name
Title | ||
|---|---|---|
Beidou navigation method based on intelligent computing and extended Kalman filter fusion |
Abstract | ||
|---|---|---|
Scientific and precise dynamic navigation is the key to improving Beidou positional accuracy of agricultural machinery. Aiming at the gross error of agricultural machinery location. First, the paper deeply explores the principle of Beidou navigation. Second, according to the PVT information (position, velocity and time) solutions of Beidou navigation system, the least square method, Kalman filter method and extended Kalman filter method were studied. Based on their own advantages, algorithms were proposed that combines the differential adaptation and extended Kalman filter. Then, based on the equivalent gain matrix and iterative solution, a robust adaptive Kalman filter model is built to verify its effectiveness in reducing gross errors. At last, the four algorithms were simulated in MATLAB and the simulation results were compared to verify that the newly-proposed method is the optimal solution algorithm. The absolute error remained 5.2 cm, meeting the preciseness limit of the agricultural machinery navigation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12652-018-1124-5 | Journal of Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Intelligent computing,Beidou navigation,Differential adaptation,Extended kalman filter,Agricultural machinery positioning | Least squares,Computer vision,Extended Kalman filter,MATLAB,Intelligent computing,Matrix (mathematics),Computer science,Navigation system,Algorithm,Kalman filter,Artificial intelligence,Approximation error | Journal |
Volume | Issue | ISSN |
10.0 | SP11.0 | 1868-5145 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongwei Tang | 1 | 0 | 0.34 |
| Jing-bo Zhao | 2 | 10 | 7.38 |
| Maoli Wang | 3 | 0 | 0.34 |
| Huijuan Hao | 4 | 0 | 0.34 |
| Xiaoning He | 5 | 0 | 0.34 |
| Yuxiao Meng | 6 | 0 | 0.34 |