Intelligent Multi-source Autonomous Navigation Method Based on GMDH Neural Network
To enhance the reconfigurability of autonomous navigation systems for aircraft operating in complex and adversarial scenarios,a multi-source autonomous navigation method based on Group Method of Data Handling(GMDH)neural network is proposed.The dynamic modeling capability of GMDH neural network is utilized to improve the state esti-mation matrix of the traditional Kalman filter,and a time-varying state transition model is constructed through the neural network,which predicts and substitutes the position and velocity information in the GNSS system time series.The proposed method allows the multi-source autonomous navigation filters to achieve smooth and rapid convergence under satellite rejec-tions,effectively mitigating error divergence during GNSS signal interruptions.Furthermore,it enhances the system's au-tonomous judgment and reconfiguration capabilities in complex mission scenarios.Simulation results show that compared to traditional loosely coupled navigation and Long Short-Term Memory neural network filter optimization algorithms,the pro-posed method improves the velocity accuracy by 27.3%and the position accuracy by 20.1%under short-term GNSS-de-nied conditions,providing a reference framework for designing end-side autonomous navigation systems within the new gen-eration of national integrated Positioning,Navigation and Timing(PNT).
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