Multilayer feedforward networks are universal approximators
Neural Networks
Approximation capabilities of multilayer feedforward networks
Neural Networks
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
| Hi-index | 0.00 |
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.