RTU Research Information System
Publikācija: Distance Estimation Using Intelligent Fusion of Navigation Data
| Publication Type | Scientific article indexed in SCOPUS or WOS database |
|---|---|
| Funding for basic activity | Unknown |
| Defending: | , |
| Publication language | English (en) |
| Title in original language | Distance Estimation Using Intelligent Fusion of Navigation Data |
| Field of research | 2. Engineering and technology |
| Sub-field of research | 2.2 Electrical engineering, Electronic engineering, Information and communication engineering |
| Authors |
Vadims Bistrovs
Ansis Klūga |
| Keywords | GPS, MEMS IMU, Intelligent Kalman filter, distance estimation |
| Abstract | INS/GPS navigation has changed due to advances in inertial manufacturing technology. This has created sensors (accelerometers, gyroscopes, magnetometers) that can be packaged with a GPS receiver/antenna for everyday use. These new manufacturing technologies are creating inertial sensors such as MEMS inertial sensors that are smaller and consume less power, but they also exhibit much larger errors comparing with their higher priced units. Kalman filtering is the main technique for combined IMU/GPS sensor data processing. Intelligent algorithm application for sensor data fusing is one of the newest areas of research. In the current paper the simplified intelligent algorithm is developed for GPS and accelerometer data combined processing. The performance of developed algorithm was checked through made experiments for estimation of distance passed by vehicle. The compare of passed distance estimation error for standard and intelligent Kalman algorithm are presented in the paper. Presented intelligent Kalman algorithm has smaller estimation error, simple in use and not demanding for computing power. |
| Reference | Bistrovs, V., Klūga, A. Distance Estimation Using Intelligent Fusion of Navigation Data. Electronics and Electrical Engineering, 2010, No.5 , pp.47-52. e-ISSN 2029-5731. ISSN 1392-1215. |
| Additional information |
Citation count:
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| ID | 7493 |
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