Application of Neural Network to the Alignment of Strapdown Inertial Navigation System

  • Meng Bai
  • Xiaoguang Zhao
  • Zeng-Guang Hou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4681)


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.


neural network initial alignment Kalman filter SINS 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Meng Bai
    • 1
  • Xiaoguang Zhao
    • 1
  • Zeng-Guang Hou
    • 1
  1. 1.Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, 100080 BeijingChina

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