Name
Title | ||
|---|---|---|
Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages. |
Abstract | ||
|---|---|---|
In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positi... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIM.2018.2805231 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Global Positioning System,Predictive models,Adaptation models,Covariance matrices,Sensor fusion,Training | Inertial navigation system,Extreme learning machine,Real-time computing,Control engineering,Kalman filter,Sensor fusion,Autoregressive integrated moving average,Global Positioning System,Fuse (electrical),Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
67 | 8 | 0018-9456 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| xu qimin | 1 | 15 | 3.42 |
| Xu Li | 2 | 17 | 5.42 |
| Ching-Yao Chan | 3 | 79 | 23.48 |