State Estimation of ALV Integrated Navigation System Based on BP Neural Network

Wang, Meiling; Fu, Yongwei

doi:10.1109/isda.2008.97

Cited by 2 publications

(2 citation statements)

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“…Similarly, the network structure growth method is used to determine the number of hidden layer nodes of the VNN structure, and the forecast accuracy is the best when the number of hidden layer nodes is 14 by simulation. Finally, the VNN structure can be obtained by training the samples, and the trained VNN structure represents the mapping relationship in (12). Table 6.…”

Section: Simulation Experiments Using Vnnmentioning

confidence: 99%

“…BPNN method is applied to the estimation for navigation state parameters of autonomous land vehicles. The corresponding simulation results show that this method can reduce the latitude and longitude error and improve the navigation accuracy effectively [12]. Since the star sensor of the surface ship integrated navigation of INS/CNS could not work normally due to the bad weather, particle swarm optimization BPNN method was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Identification Method of SINS Error Parameters Utilizing Artificial Neural Network for Hypersonic Vehicle

Guo

Zhang

et al. 2019

IEEE Access

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To improve the navigation accuracy of hypersonic vehicle, an error parameter identification method of strap-down inertial navigation system (SINS) based on artificial neural network is proposed. Firstly, the inertial measurement unit (IMU) error model and the SINS navigation calculation model are established, which can provide an accurate model basis for the error parameters identification. Then, four kinds of neural network structures with different inputs are constructed and optimized by the numerical simulation. To further improve the identification accuracy of the error parameters, the neural network method is improved by adjusting the inputs of the network structure and optimizing the initial network values based on gradient particle swarm optimization. The improved neural network method is eventually used to identify the SINS error parameters of hypersonic vehicle. The simulation results show that the neural network with position deviation, velocity deviation and attitude deviation as the inputs has the optimal network structure. The improved artificial neural network method has the highest identification accuracy for the error parameters compared with other methods, and the relative errors of the error parameters are less than 34%. Meanwhile, the navigation errors of the proposed method are equivalent to the magnitude of Kalman filter algorithm, which demonstrates the effectiveness of our method to identify the high-precision SINS error parameters and improve the navigation accuracy for hypersonic vehicle.

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Section: Simulation Experiments Using Vnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%