Abstract
The paper presents a smart integrated navigation algorithm based on neural network-aided observation, aiming at the divergence problem of integrated navigation systems caused by global positioning system (GPS) outages. The method trains the neural network when the GPS is available, and it independently rebuilds the integrated navigation system using the neural network when GPS outages occur. The output of the neural network is utilized as the measurement to build a new Kalman filter, which is used to amend the error of the strapdown inertial navigation system (SINS), and then the continuous navigation with high precision is realized. The simulation was carried out. The result demonstrated that the divergence of attitude, velocity and position were effectively controlled under this algorithm. The precision and reliability of integrated navigation systems were improved.
| Original language | English |
|---|---|
| Pages (from-to) | 71-75 |
| Number of pages | 5 |
| Journal | Gaojishu Tongxin/High Technology Letters |
| Volume | 19 |
| Issue number | 1 |
| Publication status | Published - Jan 2009 |
| Externally published | Yes |
Keywords
- GPS outages
- Integrated navigation
- Kalman filter
- RBF neural network