Abstract
Aiming at the high precision demand for GPS precise point positioning, a GPS precise point positioning method using wavelet neural network is proposed. The method adopts wavelet transform and neural network learning function, and can make the error function rapidly convergent without needing accurate system priori information and can approximate the true error model, which improve the GPS precise point positioning accuracy. Simulation results show that the proposed algorithm can shorten the GPS precise point positioning time by more than 50% and improve positioning accuracy by 90% and 50% respectively compared to the traditional least square method and the Kalman filter algorithm under static conditions. Under dynamic conditions, the proposed algorithm can improve positioning accuracy by 20%~80% compared to the traditional least square method and the Kalman filter algorithm.
Original language | English |
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Pages (from-to) | 337-341 |
Number of pages | 5 |
Journal | Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology |
Volume | 24 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jun 2016 |
Keywords
- Convergence time
- GPS precise point positioning
- Neural network
- Wavelet transform