The Application Of Wavelet Neural Networks Based On Genetic Algorithms In Integrated Navigation System

The integrated INS/GPS navigation is one of the most common used integration navigation approach aims to improve the navigation accuracy and reliability. This paper mainly focuses on the condition when GPS signal is invalid in the integrated INS/GPS navigation system. In order to keep the navigation accuracy when the INS works independently, the mathematical error prediction model of the navigation system is established and then the parameters of the INS can be amended. The genetic algorithm combined with the advanced wavelet neural network is used in the modeling on the navigation errors in the purpose of speeding the training process, enhancing universality and avoiding the model into a local minimum situation. Meanwhile, the inertial components are modeled accurately before it working.To improve the navigation accuracy, the inertial navigation system random error is modeled according to the wavelet transform scale features, and the integrated navigation system filtering algorithm based on wavelet multi-scale theory is studied. In order to keep the navigation accuracy when the INS works independently, the wavelet neural network filtering algorithm is introduced into the INS/GPS integrated navigation system.The method of using wavelet multi-resolution to analysis and process gyro signals is researched. The spectrum analysis and band energy statistics for the gyro measured signal are realized to determine the useful band of the signal, and then the gyro signals are adaptively filtered by the wavelet packet noise reduction algorithm with dynamic threshold in different band features. The wavelet theory is studied to analyze the drift characteristics of the inertial devices, and the random drift noise of the fiber optic gyro is dynamic calibrated using this method. Further more, the random drift error of the gyro is modeled based on the adaptive linear neural networks theory and real time forecasted by the wavelet neural network nonlinear prediction algorithm. The use of wavelet transform for integrated INS/GPS navigation system and the appropriate treatment to the wavelet coefficients of the INS signal layers by the wavelet threshold multi-resolution analysis effectively inhibit the effects of the INS error and improve the accuracy of integrated navigation.The dynamic filter algorithm with wavelet transform focus on the position and speed signals of INS/GPS systems is studied which could develop the trend of low-frequency signals in the form of wavelet extraction system and then get the error of INS relative to the GPS. And for the problem of integrated INS/GPS system encountered in the practical application, the wavelet neural network adaptive filter algorithm is put forward, which aims to compensate the INS navigation parameters by the calculated INS error model, thereby enhancing the independent INS work accuracy. This INS error model is based on the wavelet neural network which avoid the defect of local minimize problem compared to other kinds of network model method. And the wavelet and genetic algorithm are introduced into the system to analyze and predict the INS parameters in the GPS lost lock condition, effectively improve the navigation accuracy. When GPS is effective, the INS position, attitude and speed errors are modeled upon the INS/GPS message using the genetic algorithm based on the wavelet neural network. And when the GPS is useless, INS navigation parameters are modified by the established error prediction model. Thus the real-time precision navigation and positioning for the maneuvering aircraft comes true.