Name
Title | ||
|---|---|---|
Application of neural network to the alignment of strapdown inertial navigation system |
Abstract | ||
|---|---|---|
In this paper, a strapdown inertial navigation system (SINS) error model is introduced, and the model observability is analyzed. Due to the weak observability of SINS error model, the azimuth error can not be estimated quickly by Kalman filter. To reduce the initial alignment time, a neural network method for the initial alignment of SINS on stationary base is presented. In the method, the neural network is trained based on the data preprocessed by a Kalman filter. To smooth the neural network output data, a filter is implemented when the trained neural network is adopted as a state observer in the initial alignment. Computer simulation results illustrate that the neural network method can reduce the time of initial alignment greatly, and the estimation errors of misalignment angles are within a satisfied range. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74171-8_89 | ICIC (1) |
Keywords | Field | DocType |
error model,initial alignment time,sins error model,neural network,initial alignment,trained neural network,neural network output data,kalman filter,neural network method,strapdown inertial navigation system,azimuth error,inertial navigation system,satisfiability,state observer,data preprocessing,computer simulation | Inertial navigation system,State observer,Computer vision,Observability,Computer science,Control theory,Azimuth,Kalman filter,Artificial intelligence,Artificial neural network | Conference |
Volume | ISSN | ISBN |
4681 | 0302-9743 | 3-540-74170-4 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Bai | 1 | 9 | 1.57 |
| Xiaoguang Zhao | 2 | 54 | 18.68 |
| Zeng-Guang Hou | 3 | 2293 | 167.18 |